Merge pull request #3376 from WenmuZhou/fx_pse

add pse

configs/det/det_mv3_pse.yml

+Global:
+  use_gpu: true
+  epoch_num: 600
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/det_mv3_pse/
+  save_epoch_step: 600
+  # evaluation is run every 63 iterations
+  eval_batch_step: [ 0,63 ]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_pse/predicts_pse.txt
+
+Architecture:
+  model_type: det
+  algorithm: PSE
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: FPN
+    out_channels: 96
+  Head:
+    name: PSEHead
+    hidden_dim: 96
+    out_channels: 7
+
+Loss:
+  name: PSELoss
+  alpha: 0.7
+  ohem_ratio: 3
+  kernel_sample_mask: pred
+  reduction: none
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Step
+    learning_rate: 0.001
+    step_size: 200
+    gamma: 0.1
+  regularizer:
+    name: 'L2'
+    factor: 0.0005
+
+PostProcess:
+  name: PSEPostProcess
+  thresh: 0
+  box_thresh: 0.85
+  min_area: 16
+  box_type: box # 'box' or 'poly'
+  scale: 1
+
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - ColorJitter:
+          brightness: 0.12549019607843137
+          saturation: 0.5
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } }
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } }
+      - MakePseGt:
+          kernel_num: 7
+          min_shrink_ratio: 0.4
+          size: 640
+      - RandomCropImgMask:
+          size: [ 640,640 ]
+          main_key: gt_text
+          crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 16
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          limit_side_len: 736
+          limit_type: min
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 8
\ No newline at end of file

configs/det/det_r50_vd_pse.yml

+Global:
+  use_gpu: true
+  epoch_num: 600
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/det_r50_vd_pse/
+  save_epoch_step: 600
+  # evaluation is run every 125 iterations
+  eval_batch_step: [ 0,125 ]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
+  checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_pse/predicts_pse.txt
+
+Architecture:
+  model_type: det
+  algorithm: PSE
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 50
+  Neck:
+    name: FPN
+    out_channels: 256
+  Head:
+    name: PSEHead
+    hidden_dim: 256
+    out_channels: 7
+
+Loss:
+  name: PSELoss
+  alpha: 0.7
+  ohem_ratio: 3
+  kernel_sample_mask: pred
+  reduction: none
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Step
+    learning_rate: 0.0001
+    step_size: 200
+    gamma: 0.1
+  regularizer:
+    name: 'L2'
+    factor: 0.0005
+
+PostProcess:
+  name: PSEPostProcess
+  thresh: 0
+  box_thresh: 0.85
+  min_area: 16
+  box_type: box # 'box' or 'poly'
+  scale: 1
+
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - ColorJitter:
+          brightness: 0.12549019607843137
+          saturation: 0.5
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } }
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } }
+      - MakePseGt:
+          kernel_num: 7
+          min_shrink_ratio: 0.4
+          size: 640
+      - RandomCropImgMask:
+          size: [ 640,640 ]
+          main_key: gt_text
+          crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ]
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/icdar2015/text_localization/
+    label_file_list:
+      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          limit_side_len: 736
+          limit_type: min
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'shape', 'polys', 'ignore_tags' ]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 8
\ No newline at end of file

doc/doc_ch/algorithm_overview.md

@@ -9,11 +9,13 @@
 ### 1.文本检测算法
 
 PaddleOCR开源的文本检测算法列表：
-- [x]  DB([paper]( https://arxiv.org/abs/1911.08947)) [2]（ppocr推荐）
-- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[1]
-- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]
+- [x]  DB([paper]( https://arxiv.org/abs/1911.08947))（ppocr推荐）
+- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
+- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))
+- [x]  PSENet([paper](https://arxiv.org/abs/1903.12473v2)）
 
 在ICDAR2015文本检测公开数据集上，算法效果如下：
+
 |模型|骨干网络|precision|recall|Hmean|下载链接|
 | --- | --- | --- | --- | --- | --- |
 |EAST|ResNet50_vd|85.80%|86.71%|86.25%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
@@ -21,6 +23,8 @@ PaddleOCR开源的文本检测算法列表：
 |DB|ResNet50_vd|86.41%|78.72%|82.38%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
 |DB|MobileNetV3|77.29%|73.08%|75.12%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
 |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
+|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
+|PSE|MobileNetV3|82.20%|70.48%|75.89%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
 
 在Total-text文本检测公开数据集上，算法效果如下：
 
@@ -39,15 +43,15 @@ PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训
 ### 2.文本识别算法
 
 PaddleOCR基于动态图开源的文本识别算法列表：
-- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))[7]（ppocr推荐）
-- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
-- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
-- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
-- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))[5]
+- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))（ppocr推荐）
+- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
+- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
+- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
+- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))
 - [x]  NRTR([paper](https://arxiv.org/abs/1806.00926v2))
 - [x]  SAR([paper](https://arxiv.org/abs/1811.00751v2))
 
-参考[DTRB][3](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
+参考[DTRB](https://arxiv.org/abs/1904.01906) 文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
 
 |模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
 |---|---|---|---|---|

doc/doc_en/algorithm_overview_en.md

@@ -11,9 +11,10 @@ This tutorial lists the text detection algorithms and text recognition algorithm
 ### 1. Text Detection Algorithm
 
 PaddleOCR open source text detection algorithms list:
-- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[2]
-- [x]  DB([paper](https://arxiv.org/abs/1911.08947))[1]
-- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]
+- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
+- [x]  DB([paper](https://arxiv.org/abs/1911.08947))
+- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))
+- [x]  PSE([paper](https://arxiv.org/abs/1903.12473v2))
 
 On the ICDAR2015 dataset, the text detection result is as follows:
 
@@ -24,6 +25,8 @@ On the ICDAR2015 dataset, the text detection result is as follows:
 |DB|ResNet50_vd|86.41%|78.72%|82.38%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
 |DB|MobileNetV3|77.29%|73.08%|75.12%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
 |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
+|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
+|PSE|MobileNetV3|82.20%|70.48%|75.89%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
 
 On Total-Text dataset, the text detection result is as follows:
 
@@ -41,11 +44,11 @@ For the training guide and use of PaddleOCR text detection algorithms, please re
 ### 2. Text Recognition Algorithm
 
 PaddleOCR open-source text recognition algorithms list:
-- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))[7]
-- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
-- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
-- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
-- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))[5]
+- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))
+- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
+- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
+- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
+- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))
 - [x]  NRTR([paper](https://arxiv.org/abs/1806.00926v2))
 - [x]  SAR([paper](https://arxiv.org/abs/1811.00751v2))
 

ppocr/data/imaug/ColorJitter.py

+# copyright (c) 2020 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.
+# 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.
+from paddle.vision.transforms import ColorJitter as pp_ColorJitter
+
+__all__  = ['ColorJitter']
+
+class ColorJitter(object):
+    def __init__(self, brightness=0, contrast=0, saturation=0, hue=0,**kwargs):
+        self.aug = pp_ColorJitter(brightness, contrast, saturation, hue)
+
+    def __call__(self, data):
+        image = data['image']
+        image = self.aug(image)
+        data['image'] = image
+        return data

ppocr/data/imaug/__init__.py

@@ -19,11 +19,13 @@ from __future__ import unicode_literals
 from .iaa_augment import IaaAugment
 from .make_border_map import MakeBorderMap
 from .make_shrink_map import MakeShrinkMap
-from .random_crop_data import EastRandomCropData, PSERandomCrop
+from .random_crop_data import EastRandomCropData, RandomCropImgMask
+from .make_pse_gt import MakePseGt
 
 from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg
 from .randaugment import RandAugment
 from .copy_paste import CopyPaste
+from .ColorJitter import ColorJitter
 from .operators import *
 from .label_ops import *
 

ppocr/data/imaug/make_pse_gt.py

+# -*- coding:utf-8 -*- 
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import cv2
+import numpy as np
+import pyclipper
+from shapely.geometry import Polygon
+
+__all__ = ['MakePseGt']
+
+class MakePseGt(object):
+    r'''
+    Making binary mask from detection data with ICDAR format.
+    Typically following the process of class `MakeICDARData`.
+    '''
+
+    def __init__(self, kernel_num=7, size=640, min_shrink_ratio=0.4, **kwargs):
+        self.kernel_num = kernel_num
+        self.min_shrink_ratio = min_shrink_ratio
+        self.size = size
+
+    def __call__(self, data):
+
+        image = data['image']
+        text_polys = data['polys']
+        ignore_tags = data['ignore_tags']
+
+        h, w, _ = image.shape
+        short_edge = min(h, w)
+        if short_edge < self.size:
+            # keep short_size >= self.size
+            scale = self.size / short_edge
+            image = cv2.resize(image, dsize=None, fx=scale, fy=scale)
+            text_polys *= scale
+
+        gt_kernels = []
+        for i in range(1,self.kernel_num+1):
+            # s1->sn, from big to small
+            rate = 1.0 - (1.0 - self.min_shrink_ratio) / (self.kernel_num - 1) * i
+            text_kernel, ignore_tags = self.generate_kernel(image.shape[0:2], rate, text_polys, ignore_tags)
+            gt_kernels.append(text_kernel)
+
+        training_mask = np.ones(image.shape[0:2], dtype='uint8')
+        for i in range(text_polys.shape[0]):
+            if ignore_tags[i]:
+                cv2.fillPoly(training_mask, text_polys[i].astype(np.int32)[np.newaxis, :, :], 0)
+
+        gt_kernels = np.array(gt_kernels)
+        gt_kernels[gt_kernels > 0] = 1
+
+        data['image'] = image
+        data['polys'] = text_polys
+        data['gt_kernels'] = gt_kernels[0:]
+        data['gt_text'] = gt_kernels[0]
+        data['mask'] = training_mask.astype('float32')
+        return data
+
+    def generate_kernel(self, img_size, shrink_ratio, text_polys, ignore_tags=None):
+        h, w = img_size
+        text_kernel = np.zeros((h, w), dtype=np.float32)
+        for i, poly in enumerate(text_polys):
+            polygon = Polygon(poly)
+            distance = polygon.area * (1 - shrink_ratio * shrink_ratio) / (polygon.length + 1e-6)
+            subject = [tuple(l) for l in poly]
+            pco = pyclipper.PyclipperOffset()
+            pco.AddPath(subject, pyclipper.JT_ROUND,
+                        pyclipper.ET_CLOSEDPOLYGON)
+            shrinked = np.array(pco.Execute(-distance))
+
+            if len(shrinked) == 0 or shrinked.size == 0:
+                if ignore_tags is not None:
+                    ignore_tags[i] = True
+                continue
+            try:
+                shrinked = np.array(shrinked[0]).reshape(-1, 2)
+            except:
+                if ignore_tags is not None:
+                    ignore_tags[i] = True
+                continue
+            cv2.fillPoly(text_kernel, [shrinked.astype(np.int32)], i + 1)
+        return text_kernel, ignore_tags

ppocr/data/imaug/random_crop_data.py

@@ -164,47 +164,55 @@ class EastRandomCropData(object):
         return data
 
 
-class PSERandomCrop(object):
-    def __init__(self, size, **kwargs):
+class RandomCropImgMask(object):
+    def __init__(self, size, main_key, crop_keys, p=3 / 8, **kwargs):
         self.size = size
+        self.main_key = main_key
+        self.crop_keys = crop_keys
+        self.p = p
 
     def __call__(self, data):
-        imgs = data['imgs']
+        image = data['image']
 
-        h, w = imgs[0].shape[0:2]
+        h, w = image.shape[0:2]
         th, tw = self.size
         if w == tw and h == th:
-            return imgs
+            return data
 
-        # label中存在文本实例，并且按照概率进行裁剪，使用threshold_label_map控制
-        if np.max(imgs[2]) > 0 and random.random() > 3 / 8:
-            # 文本实例的左上角点
-            tl = np.min(np.where(imgs[2] > 0), axis=1) - self.size
+        mask = data[self.main_key]
+        if np.max(mask) > 0 and random.random() > self.p:
+            # make sure to crop the text region
+            tl = np.min(np.where(mask > 0), axis=1) - (th, tw)
             tl[tl < 0] = 0
-            # 文本实例的右下角点
-            br = np.max(np.where(imgs[2] > 0), axis=1) - self.size
+            br = np.max(np.where(mask > 0), axis=1) - (th, tw)
             br[br < 0] = 0
-            # 保证选到右下角点时，有足够的距离进行crop
+
             br[0] = min(br[0], h - th)
             br[1] = min(br[1], w - tw)
 
-            for _ in range(50000):
-                i = random.randint(tl[0], br[0])
-                j = random.randint(tl[1], br[1])
-                # 保证shrink_label_map有文本
-                if imgs[1][i:i + th, j:j + tw].sum() <= 0:
-                    continue
+            i = random.randint(tl[0], br[0]) if tl[0] < br[0] else 0
+            j = random.randint(tl[1], br[1]) if tl[1] < br[1] else 0
         else:
-                    break
-        else:
-            i = random.randint(0, h - th)
-            j = random.randint(0, w - tw)
+            i = random.randint(0, h - th) if h - th > 0 else 0
+            j = random.randint(0, w - tw) if w - tw > 0 else 0
 
         # return i, j, th, tw
-        for idx in range(len(imgs)):
-            if len(imgs[idx].shape) == 3:
-                imgs[idx] = imgs[idx][i:i + th, j:j + tw, :]
+        for k in data:
+            if k in self.crop_keys:
+                if len(data[k].shape) == 3:
+                    if np.argmin(data[k].shape) == 0:
+                        img = data[k][:, i:i + th, j:j + tw]
+                        if img.shape[1] != img.shape[2]:
+                            a = 1
+                    elif np.argmin(data[k].shape) == 2:
+                        img = data[k][i:i + th, j:j + tw, :]
+                        if img.shape[1] != img.shape[0]:
+                            a = 1
+                    else:
+                        img = data[k]
                 else:
-                imgs[idx] = imgs[idx][i:i + th, j:j + tw]
-        data['imgs'] = imgs
+                    img = data[k][i:i + th, j:j + tw]
+                    if img.shape[0] != img.shape[1]:
+                        a = 1
+                data[k] = img
         return data

ppocr/losses/__init__.py

@@ -20,6 +20,7 @@ import paddle.nn as nn
 from .det_db_loss import DBLoss
 from .det_east_loss import EASTLoss
 from .det_sast_loss import SASTLoss
+from .det_pse_loss import PSELoss
 
 # rec loss
 from .rec_ctc_loss import CTCLoss
@@ -42,10 +43,12 @@ from .combined_loss import CombinedLoss
 # table loss
 from .table_att_loss import TableAttentionLoss
 
+
 def build_loss(config):
     support_dict = [
-        'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss',
-        'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss', 'TableAttentionLoss', 'SARLoss'
+        'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss',
+        'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss',
+        'TableAttentionLoss', 'SARLoss'
     ]
 
     config = copy.deepcopy(config)

ppocr/losses/det_basic_loss.py

@@ -75,12 +75,6 @@ class BalanceLoss(nn.Layer):
             mask (variable): masked maps.
         return: (variable) balanced loss
         """
-        # if self.main_loss_type in ['DiceLoss']:
-        #     # For the loss that returns to scalar value, perform ohem on the mask
-        #     mask = ohem_batch(pred, gt, mask, self.negative_ratio)
-        #     loss = self.loss(pred, gt, mask)
-        #     return loss
-
         positive = gt * mask
         negative = (1 - gt) * mask
 
@@ -154,52 +148,3 @@ class BCELoss(nn.Layer):
     def forward(self, input, label, mask=None, weight=None, name=None):
         loss = F.binary_cross_entropy(input, label, reduction=self.reduction)
         return loss
\ No newline at end of file
-
-
-def ohem_single(score, gt_text, training_mask, ohem_ratio):
-    pos_num = (int)(np.sum(gt_text > 0.5)) - (
-        int)(np.sum((gt_text > 0.5) & (training_mask <= 0.5)))
-
-    if pos_num == 0:
-        # selected_mask = gt_text.copy() * 0 # may be not good
-        selected_mask = training_mask
-        selected_mask = selected_mask.reshape(
-            1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32')
-        return selected_mask
-
-    neg_num = (int)(np.sum(gt_text <= 0.5))
-    neg_num = (int)(min(pos_num * ohem_ratio, neg_num))
-
-    if neg_num == 0:
-        selected_mask = training_mask
-        selected_mask = selected_mask.reshape(
-            1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32')
-        return selected_mask
-
-    neg_score = score[gt_text <= 0.5]
-    # 将负样本得分从高到低排序
-    neg_score_sorted = np.sort(-neg_score)
-    threshold = -neg_score_sorted[neg_num - 1]
-    # 选出 得分高的 负样本 和正样本 的 mask
-    selected_mask = ((score >= threshold) |
-                     (gt_text > 0.5)) & (training_mask > 0.5)
-    selected_mask = selected_mask.reshape(
-        1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32')
-    return selected_mask
-
-
-def ohem_batch(scores, gt_texts, training_masks, ohem_ratio):
-    scores = scores.numpy()
-    gt_texts = gt_texts.numpy()
-    training_masks = training_masks.numpy()
-
-    selected_masks = []
-    for i in range(scores.shape[0]):
-        selected_masks.append(
-            ohem_single(scores[i, :, :], gt_texts[i, :, :], training_masks[
-                i, :, :], ohem_ratio))
-
-    selected_masks = np.concatenate(selected_masks, 0)
-    selected_masks = paddle.to_tensor(selected_masks)
-
-    return selected_masks

ppocr/losses/det_pse_loss.py

+# 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.
+# 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 paddle
+from paddle import nn
+from paddle.nn import functional as F
+import numpy as np
+from ppocr.utils.iou import iou
+
+
+class PSELoss(nn.Layer):
+    def __init__(self,
+                 alpha,
+                 ohem_ratio=3,
+                 kernel_sample_mask='pred',
+                 reduction='sum',
+                 eps=1e-6,
+                 **kwargs):
+        """Implement PSE Loss.
+        """
+        super(PSELoss, self).__init__()
+        assert reduction in ['sum', 'mean', 'none']
+        self.alpha = alpha
+        self.ohem_ratio = ohem_ratio
+        self.kernel_sample_mask = kernel_sample_mask
+        self.reduction = reduction
+        self.eps = eps
+
+    def forward(self, outputs, labels):
+        predicts = outputs['maps']
+        predicts = F.interpolate(predicts, scale_factor=4)
+
+        texts = predicts[:, 0, :, :]
+        kernels = predicts[:, 1:, :, :]
+        gt_texts, gt_kernels, training_masks = labels[1:]
+
+        # text loss
+        selected_masks = self.ohem_batch(texts, gt_texts, training_masks)
+
+        loss_text = self.dice_loss(texts, gt_texts, selected_masks)
+        iou_text = iou((texts > 0).astype('int64'),
+                       gt_texts,
+                       training_masks,
+                       reduce=False)
+        losses = dict(loss_text=loss_text, iou_text=iou_text)
+
+        # kernel loss
+        loss_kernels = []
+        if self.kernel_sample_mask == 'gt':
+            selected_masks = gt_texts * training_masks
+        elif self.kernel_sample_mask == 'pred':
+            selected_masks = (
+                F.sigmoid(texts) > 0.5).astype('float32') * training_masks
+
+        for i in range(kernels.shape[1]):
+            kernel_i = kernels[:, i, :, :]
+            gt_kernel_i = gt_kernels[:, i, :, :]
+            loss_kernel_i = self.dice_loss(kernel_i, gt_kernel_i,
+                                           selected_masks)
+            loss_kernels.append(loss_kernel_i)
+        loss_kernels = paddle.mean(paddle.stack(loss_kernels, axis=1), axis=1)
+        iou_kernel = iou((kernels[:, -1, :, :] > 0).astype('int64'),
+                         gt_kernels[:, -1, :, :],
+                         training_masks * gt_texts,
+                         reduce=False)
+        losses.update(dict(loss_kernels=loss_kernels, iou_kernel=iou_kernel))
+        loss = self.alpha * loss_text + (1 - self.alpha) * loss_kernels
+        losses['loss'] = loss
+        if self.reduction == 'sum':
+            losses = {x: paddle.sum(v) for x, v in losses.items()}
+        elif self.reduction == 'mean':
+            losses = {x: paddle.mean(v) for x, v in losses.items()}
+        return losses
+
+    def dice_loss(self, input, target, mask):
+        input = F.sigmoid(input)
+
+        input = input.reshape([input.shape[0], -1])
+        target = target.reshape([target.shape[0], -1])
+        mask = mask.reshape([mask.shape[0], -1])
+
+        input = input * mask
+        target = target * mask
+
+        a = paddle.sum(input * target, 1)
+        b = paddle.sum(input * input, 1) + self.eps
+        c = paddle.sum(target * target, 1) + self.eps
+        d = (2 * a) / (b + c)
+        return 1 - d
+
+    def ohem_single(self, score, gt_text, training_mask, ohem_ratio=3):
+        pos_num = int(paddle.sum((gt_text > 0.5).astype('float32'))) - int(
+            paddle.sum(
+                paddle.logical_and((gt_text > 0.5), (training_mask <= 0.5))
+                .astype('float32')))
+
+        if pos_num == 0:
+            selected_mask = training_mask
+            selected_mask = selected_mask.reshape(
+                [1, selected_mask.shape[0], selected_mask.shape[1]]).astype(
+                    'float32')
+            return selected_mask
+
+        neg_num = int(paddle.sum((gt_text <= 0.5).astype('float32')))
+        neg_num = int(min(pos_num * ohem_ratio, neg_num))
+
+        if neg_num == 0:
+            selected_mask = training_mask
+            selected_mask = selected_mask.view(
+                1, selected_mask.shape[0],
+                selected_mask.shape[1]).astype('float32')
+            return selected_mask
+
+        neg_score = paddle.masked_select(score, gt_text <= 0.5)
+        neg_score_sorted = paddle.sort(-neg_score)
+        threshold = -neg_score_sorted[neg_num - 1]
+
+        selected_mask = paddle.logical_and(
+            paddle.logical_or((score >= threshold), (gt_text > 0.5)),
+            (training_mask > 0.5))
+        selected_mask = selected_mask.reshape(
+            [1, selected_mask.shape[0], selected_mask.shape[1]]).astype(
+                'float32')
+        return selected_mask
+
+    def ohem_batch(self, scores, gt_texts, training_masks, ohem_ratio=3):
+        selected_masks = []
+        for i in range(scores.shape[0]):
+            selected_masks.append(
+                self.ohem_single(scores[i, :, :], gt_texts[i, :, :],
+                                 training_masks[i, :, :], ohem_ratio))
+
+        selected_masks = paddle.concat(selected_masks, 0).astype('float32')
+        return selected_masks

ppocr/metrics/eval_det_iou.py

@@ -169,21 +169,10 @@ class DetectionIoUEvaluator(object):
         numGlobalCareDet += numDetCare
 
         perSampleMetrics = {
-            'precision': precision,
-            'recall': recall,
-            'hmean': hmean,
-            'pairs': pairs,
-            'iouMat': [] if len(detPols) > 100 else iouMat.tolist(),
-            'gtPolPoints': gtPolPoints,
-            'detPolPoints': detPolPoints,
             'gtCare': numGtCare,
             'detCare': numDetCare,
-            'gtDontCare': gtDontCarePolsNum,
-            'detDontCare': detDontCarePolsNum,
             'detMatched': detMatched,
-            'evaluationLog': evaluationLog
         }
-
         return perSampleMetrics
 
     def combine_results(self, results):

ppocr/modeling/heads/__init__.py

@@ -20,6 +20,7 @@ def build_head(config):
     from .det_db_head import DBHead
     from .det_east_head import EASTHead
     from .det_sast_head import SASTHead
+    from .det_pse_head import PSEHead
     from .e2e_pg_head import PGHead
 
     # rec head
@@ -32,8 +33,9 @@ def build_head(config):
     # cls head
     from .cls_head import ClsHead
     support_dict = [
-        'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
-        'SRNHead', 'PGHead', 'Transformer', 'TableAttentionHead', 'SARHead'
+        'DBHead', 'PSEHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead',
+        'AttentionHead', 'SRNHead', 'PGHead', 'Transformer',
+        'TableAttentionHead', 'SARHead'
     ]
 
     #table head

ppocr/modeling/heads/det_pse_head.py

+# copyright (c) 2020 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.
+# 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.
+from paddle import nn
+
+
+class PSEHead(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 hidden_dim=256,
+                 out_channels=7,
+                 **kwargs):
+        super(PSEHead, self).__init__()
+        self.conv1 = nn.Conv2D(in_channels, hidden_dim, kernel_size=3, stride=1, padding=1)
+        self.bn1 = nn.BatchNorm2D(hidden_dim)
+        self.relu1 = nn.ReLU()
+
+        self.conv2 = nn.Conv2D(hidden_dim, out_channels, kernel_size=1, stride=1, padding=0)
+
+
+    def forward(self, x, **kwargs):
+        out = self.conv1(x)
+        out = self.relu1(self.bn1(out))
+        out = self.conv2(out)
+        return {'maps': out}

ppocr/modeling/necks/__init__.py

@@ -22,7 +22,8 @@ def build_neck(config):
     from .rnn import SequenceEncoder
     from .pg_fpn import PGFPN
     from .table_fpn import TableFPN
-    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN', 'TableFPN']
+    from .fpn import FPN
+    support_dict = ['FPN','DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN', 'TableFPN']
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('neck only support {}'.format(

ppocr/modeling/necks/fpn.py

+# 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.
+# 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 paddle.nn as nn
+import paddle
+import math
+import paddle.nn.functional as F
+
+class Conv_BN_ReLU(nn.Layer):
+    def __init__(self, in_planes, out_planes, kernel_size=1, stride=1, padding=0):
+        super(Conv_BN_ReLU, self).__init__()
+        self.conv = nn.Conv2D(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
+                              bias_attr=False)
+        self.bn = nn.BatchNorm2D(out_planes, momentum=0.1)
+        self.relu = nn.ReLU()
+
+        for m in self.sublayers():
+            if isinstance(m, nn.Conv2D):
+                n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
+                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', default_initializer=paddle.nn.initializer.Normal(0, math.sqrt(2. / n)))
+            elif isinstance(m, nn.BatchNorm2D):
+                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', default_initializer=paddle.nn.initializer.Constant(1.0))
+                m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32', default_initializer=paddle.nn.initializer.Constant(0.0))
+
+    def forward(self, x):
+        return self.relu(self.bn(self.conv(x)))
+
+class FPN(nn.Layer):
+    def __init__(self, in_channels, out_channels):
+        super(FPN, self).__init__()
+
+        # Top layer
+        self.toplayer_ = Conv_BN_ReLU(in_channels[3], out_channels, kernel_size=1, stride=1, padding=0)
+        # Lateral layers
+        self.latlayer1_ = Conv_BN_ReLU(in_channels[2], out_channels, kernel_size=1, stride=1, padding=0)
+
+        self.latlayer2_ = Conv_BN_ReLU(in_channels[1], out_channels, kernel_size=1, stride=1, padding=0)
+
+        self.latlayer3_ = Conv_BN_ReLU(in_channels[0], out_channels, kernel_size=1, stride=1, padding=0)
+
+        # Smooth layers
+        self.smooth1_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        self.smooth2_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        self.smooth3_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+
+        self.out_channels = out_channels * 4
+        for m in self.sublayers():
+            if isinstance(m, nn.Conv2D):
+                n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
+                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32',
+                                                   default_initializer=paddle.nn.initializer.Normal(0,
+                                                                                                    math.sqrt(2. / n)))
+            elif isinstance(m, nn.BatchNorm2D):
+                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32',
+                                                   default_initializer=paddle.nn.initializer.Constant(1.0))
+                m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32',
+                                                 default_initializer=paddle.nn.initializer.Constant(0.0))
+
+    def _upsample(self, x, scale=1):
+        return F.upsample(x, scale_factor=scale, mode='bilinear')
+
+    def _upsample_add(self, x, y, scale=1):
+        return F.upsample(x, scale_factor=scale, mode='bilinear') + y
+
+    def forward(self, x):
+        f2, f3, f4, f5 = x
+        p5 = self.toplayer_(f5)
+
+        f4 = self.latlayer1_(f4)
+        p4 = self._upsample_add(p5, f4,2)
+        p4 = self.smooth1_(p4)
+
+        f3 = self.latlayer2_(f3)
+        p3 = self._upsample_add(p4, f3,2)
+        p3 = self.smooth2_(p3)
+
+        f2 = self.latlayer3_(f2)
+        p2 = self._upsample_add(p3, f2,2)
+        p2 = self.smooth3_(p2)
+
+        p3 = self._upsample(p3, 2)
+        p4 = self._upsample(p4, 4)
+        p5 = self._upsample(p5, 8)
+
+        fuse = paddle.concat([p2, p3, p4, p5], axis=1)
+        return fuse
\ No newline at end of file

ppocr/postprocess/__init__.py

@@ -28,12 +28,14 @@ from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, Di
     TableLabelDecode, SARLabelDecode
 from .cls_postprocess import ClsPostProcess
 from .pg_postprocess import PGPostProcess
+from .pse_postprocess import PSEPostProcess
+
 
 def build_post_process(config, global_config=None):
     support_dict = [
-        'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
-        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
-        'DistillationCTCLabelDecode', 'TableLabelDecode',
+        'DBPostProcess', 'PSEPostProcess', 'EASTPostProcess', 'SASTPostProcess',
+        'CTCLabelDecode', 'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode',
+        'PGPostProcess', 'DistillationCTCLabelDecode', 'TableLabelDecode',
         'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode'
     ]
 

ppocr/postprocess/pse_postprocess/__init__.py

+# 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.
+# 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.
+
+from .pse_postprocess import PSEPostProcess
\ No newline at end of file

ppocr/postprocess/pse_postprocess/pse/README.md

+## 编译
+code from https://github.com/whai362/pan_pp.pytorch
+```python
+python3 setup.py build_ext --inplace
+```

ppocr/postprocess/pse_postprocess/pse/__init__.py

+# copyright (c) 2020 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.
+# 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 sys
+import os
+import subprocess
+
+python_path = sys.executable
+
+if subprocess.call('cd ppocr/postprocess/pse_postprocess/pse;{} setup.py build_ext --inplace;cd -'.format(python_path), shell=True) != 0:
+    raise RuntimeError('Cannot compile pse: {}'.format(os.path.dirname(os.path.realpath(__file__))))
+
+from .pse import pse
\ No newline at end of file

ppocr/postprocess/pse_postprocess/pse/pse.pyx

+
+import numpy as np
+import cv2
+cimport numpy as np
+cimport cython
+cimport libcpp
+cimport libcpp.pair
+cimport libcpp.queue
+from libcpp.pair cimport *
+from libcpp.queue  cimport *
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cdef np.ndarray[np.int32_t, ndim=2] _pse(np.ndarray[np.uint8_t, ndim=3] kernels,
+                                         np.ndarray[np.int32_t, ndim=2] label,
+                                         int kernel_num,
+                                         int label_num,
+                                         float min_area=0):
+    cdef np.ndarray[np.int32_t, ndim=2] pred
+    pred = np.zeros((label.shape[0], label.shape[1]), dtype=np.int32)
+
+    for label_idx in range(1, label_num):
+        if np.sum(label == label_idx) < min_area:
+            label[label == label_idx] = 0
+
+    cdef libcpp.queue.queue[libcpp.pair.pair[np.int16_t,np.int16_t]] que = \
+        queue[libcpp.pair.pair[np.int16_t,np.int16_t]]()
+    cdef libcpp.queue.queue[libcpp.pair.pair[np.int16_t,np.int16_t]] nxt_que = \
+        queue[libcpp.pair.pair[np.int16_t,np.int16_t]]()
+    cdef np.int16_t* dx = [-1, 1, 0, 0]
+    cdef np.int16_t* dy = [0, 0, -1, 1]
+    cdef np.int16_t tmpx, tmpy
+
+    points = np.array(np.where(label > 0)).transpose((1, 0))
+    for point_idx in range(points.shape[0]):
+        tmpx, tmpy = points[point_idx, 0], points[point_idx, 1]
+        que.push(pair[np.int16_t,np.int16_t](tmpx, tmpy))
+        pred[tmpx, tmpy] = label[tmpx, tmpy]
+
+    cdef libcpp.pair.pair[np.int16_t,np.int16_t] cur
+    cdef int cur_label
+    for kernel_idx in range(kernel_num - 1, -1, -1):
+        while not que.empty():
+            cur = que.front()
+            que.pop()
+            cur_label = pred[cur.first, cur.second]
+
+            is_edge = True
+            for j in range(4):
+                tmpx = cur.first + dx[j]
+                tmpy = cur.second + dy[j]
+                if tmpx < 0 or tmpx >= label.shape[0] or tmpy < 0 or tmpy >= label.shape[1]:
+                    continue
+                if kernels[kernel_idx, tmpx, tmpy] == 0 or pred[tmpx, tmpy] > 0:
+                    continue
+
+                que.push(pair[np.int16_t,np.int16_t](tmpx, tmpy))
+                pred[tmpx, tmpy] = cur_label
+                is_edge = False
+            if is_edge:
+                nxt_que.push(cur)
+
+        que, nxt_que = nxt_que, que
+
+    return pred
+
+def pse(kernels, min_area):
+    kernel_num = kernels.shape[0]
+    label_num, label = cv2.connectedComponents(kernels[-1], connectivity=4)
+    return _pse(kernels[:-1], label, kernel_num, label_num, min_area)
\ No newline at end of file

ppocr/postprocess/pse_postprocess/pse/setup.py

+from distutils.core import setup, Extension
+from Cython.Build import cythonize
+import numpy
+
+setup(ext_modules=cythonize(Extension(
+    'pse',
+    sources=['pse.pyx'],
+    language='c++',
+    include_dirs=[numpy.get_include()],
+    library_dirs=[],
+    libraries=[],
+    extra_compile_args=['-O3'],
+    extra_link_args=[]
+)))

ppocr/postprocess/pse_postprocess/pse_postprocess.py

+# 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.
+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import cv2
+import paddle
+from paddle.nn import functional as F
+
+from ppocr.postprocess.pse_postprocess.pse import pse
+
+
+class PSEPostProcess(object):
+    """
+    The post process for PSE.
+    """
+
+    def __init__(self,
+                 thresh=0.5,
+                 box_thresh=0.85,
+                 min_area=16,
+                 box_type='box',
+                 scale=4,
+                 **kwargs):
+        assert box_type in ['box', 'poly'], 'Only box and poly is supported'
+        self.thresh = thresh
+        self.box_thresh = box_thresh
+        self.min_area = min_area
+        self.box_type = box_type
+        self.scale = scale
+
+    def __call__(self, outs_dict, shape_list):
+        pred = outs_dict['maps']
+        if not isinstance(pred, paddle.Tensor):
+            pred = paddle.to_tensor(pred)
+        pred = F.interpolate(pred, scale_factor=4 // self.scale, mode='bilinear')
+
+        score = F.sigmoid(pred[:, 0, :, :])
+
+        kernels = (pred > self.thresh).astype('float32')
+        text_mask = kernels[:, 0, :, :]
+        kernels[:, 0:, :, :] = kernels[:, 0:, :, :] * text_mask
+
+        score = score.numpy()
+        kernels = kernels.numpy().astype(np.uint8)
+
+        boxes_batch = []
+        for batch_index in range(pred.shape[0]):
+            boxes, scores = self.boxes_from_bitmap(score[batch_index], kernels[batch_index], shape_list[batch_index])
+
+            boxes_batch.append({'points': boxes, 'scores': scores})
+        return boxes_batch
+
+    def boxes_from_bitmap(self, score, kernels, shape):
+        label = pse(kernels, self.min_area)
+        return self.generate_box(score, label, shape)
+
+    def generate_box(self, score, label, shape):
+        src_h, src_w, ratio_h, ratio_w = shape
+        label_num = np.max(label) + 1
+
+        boxes = []
+        scores = []
+        for i in range(1, label_num):
+            ind = label == i
+            points = np.array(np.where(ind)).transpose((1, 0))[:, ::-1]
+
+            if points.shape[0] < self.min_area:
+                label[ind] = 0
+                continue
+
+            score_i = np.mean(score[ind])
+            if score_i < self.box_thresh:
+                label[ind] = 0
+                continue
+
+            if self.box_type == 'box':
+                rect = cv2.minAreaRect(points)
+                bbox = cv2.boxPoints(rect)
+            elif self.box_type == 'poly':
+                box_height = np.max(points[:, 1]) + 10
+                box_width = np.max(points[:, 0]) + 10
+
+                mask = np.zeros((box_height, box_width), np.uint8)
+                mask[points[:, 1], points[:, 0]] = 255
+
+                contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+                bbox = np.squeeze(contours[0], 1)
+            else:
+                raise NotImplementedError
+
+            bbox[:, 0] = np.clip(
+                np.round(bbox[:, 0] / ratio_w), 0, src_w)
+            bbox[:, 1] = np.clip(
+                np.round(bbox[:, 1] / ratio_h), 0, src_h)
+            boxes.append(bbox)
+            scores.append(score_i)
+        return boxes, scores

ppocr/utils/iou.py

+# copyright (c) 2020 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.
+# 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 paddle
+
+EPS = 1e-6
+
+def iou_single(a, b, mask, n_class):
+    valid = mask == 1
+    a = a.masked_select(valid)
+    b = b.masked_select(valid)
+    miou = []
+    for i in range(n_class):
+        if a.shape == [0] and a.shape==b.shape:
+            inter = paddle.to_tensor(0.0)
+            union = paddle.to_tensor(0.0)
+        else:
+            inter = ((a == i).logical_and(b == i)).astype('float32')
+            union = ((a == i).logical_or(b == i)).astype('float32')
+        miou.append(paddle.sum(inter) / (paddle.sum(union) + EPS))
+    miou = sum(miou) / len(miou)
+    return miou
+
+def iou(a, b, mask, n_class=2, reduce=True):
+    batch_size = a.shape[0]
+
+    a = a.reshape([batch_size, -1])
+    b = b.reshape([batch_size, -1])
+    mask = mask.reshape([batch_size, -1])
+
+    iou = paddle.zeros((batch_size,), dtype='float32')
+    for i in range(batch_size):
+        iou[i] = iou_single(a[i], b[i], mask[i], n_class)
+
+    if reduce:
+        iou = paddle.mean(iou)
+    return iou
\ No newline at end of file

requirements.txt

@@ -8,6 +8,7 @@ numpy
 visualdl
 python-Levenshtein
 opencv-contrib-python==4.4.0.46
+cython
 lxml
 premailer
 openpyxl
\ No newline at end of file

tools/infer/predict_det.py

@@ -89,6 +89,14 @@ class TextDetector(object):
                 postprocess_params["sample_pts_num"] = 2
                 postprocess_params["expand_scale"] = 1.0
                 postprocess_params["shrink_ratio_of_width"] = 0.3
+        elif self.det_algorithm == "PSE":
+            postprocess_params['name'] = 'PSEPostProcess'
+            postprocess_params["thresh"] = args.det_pse_thresh
+            postprocess_params["box_thresh"] = args.det_pse_box_thresh
+            postprocess_params["min_area"] = args.det_pse_min_area
+            postprocess_params["box_type"] = args.det_pse_box_type
+            postprocess_params["scale"] = args.det_pse_scale
+            self.det_pse_box_type = args.det_pse_box_type
         else:
             logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
             sys.exit(0)
@@ -209,7 +217,7 @@ class TextDetector(object):
             preds['f_score'] = outputs[1]
             preds['f_tco'] = outputs[2]
             preds['f_tvo'] = outputs[3]
-        elif self.det_algorithm == 'DB':
+        elif self.det_algorithm in ['DB', 'PSE']:
             preds['maps'] = outputs[0]
         else:
             raise NotImplementedError
@@ -217,7 +225,9 @@ class TextDetector(object):
         #self.predictor.try_shrink_memory()
         post_result = self.postprocess_op(preds, shape_list)
         dt_boxes = post_result[0]['points']
-        if self.det_algorithm == "SAST" and self.det_sast_polygon:
+        if (self.det_algorithm == "SAST" and
+                self.det_sast_polygon) or (self.det_algorithm == "PSE" and
+                                           self.det_pse_box_type == 'poly'):
             dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
         else:
             dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)

tools/infer/utility.py

@@ -63,6 +63,13 @@ def init_args():
     parser.add_argument("--det_sast_nms_thresh", type=float, default=0.2)
     parser.add_argument("--det_sast_polygon", type=str2bool, default=False)
 
+    # PSE parmas
+    parser.add_argument("--det_pse_thresh", type=float, default=0)
+    parser.add_argument("--det_pse_box_thresh", type=float, default=0.85)
+    parser.add_argument("--det_pse_min_area", type=float, default=16)
+    parser.add_argument("--det_pse_box_type", type=str, default='box')
+    parser.add_argument("--det_pse_scale", type=int, default=1)
+
     # params for text recognizer
     parser.add_argument("--rec_algorithm", type=str, default='CRNN')
     parser.add_argument("--rec_model_dir", type=str)

tools/program.py

@@ -402,7 +402,7 @@ def preprocess(is_train=False):
     alg = config['Architecture']['algorithm']
     assert alg in [
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
-        'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR'
+        'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE'
     ]
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'