提交 d036c91a 编写于 作者: J Jethong

support two postprocess

上级 f67e6e13
...@@ -59,6 +59,7 @@ Optimizer: ...@@ -59,6 +59,7 @@ Optimizer:
PostProcess: PostProcess:
name: PGPostProcess name: PGPostProcess
score_thresh: 0.5 score_thresh: 0.5
mode: fast # fast or slow two ways
Metric: Metric:
name: E2EMetric name: E2EMetric
gt_mat_dir: # the dir of gt_mat gt_mat_dir: # the dir of gt_mat
......
...@@ -28,13 +28,10 @@ inference 模型(`paddle.jit.save`保存的模型) ...@@ -28,13 +28,10 @@ inference 模型(`paddle.jit.save`保存的模型)
- [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理) - [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
- [5. 多语言模型的推理](#多语言模型的推理) - [5. 多语言模型的推理](#多语言模型的推理)
- [四、端到端模型推理](#端到端模型推理) - [四、方向分类模型推理](#方向识别模型推理)
- [1. PGNet端到端模型推理](#PGNet端到端模型推理)
- [五、方向分类模型推理](#方向识别模型推理)
- [1. 方向分类模型推理](#方向分类模型推理) - [1. 方向分类模型推理](#方向分类模型推理)
- [、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理) - [、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理)
- [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理) - [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
- [2. 其他模型推理](#其他模型推理) - [2. 其他模型推理](#其他模型推理)
...@@ -362,38 +359,8 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" - ...@@ -362,38 +359,8 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" -
Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904) Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
``` ```
<a name="端到端模型推理"></a>
## 四、端到端模型推理
端到端模型推理,默认使用PGNet模型的配置参数。当不使用PGNet模型时,在推理时,需要通过传入相应的参数进行算法适配,细节参考下文。
<a name="PGNet端到端模型推理"></a>
### 1. PGNet端到端模型推理
#### (1). 四边形文本检测模型(ICDAR2015)
首先将PGNet端到端训练过程中保存的模型,转换成inference model。以基于Resnet50_vd骨干网络,在ICDAR2015英文数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar)),可以使用如下命令进行转换:
```
python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/iter_epoch_450 Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
```
**PGNet端到端模型推理,需要设置参数`--e2e_algorithm="PGNet"`**,可以执行如下命令:
```
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img_10.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=False
```
可视化文本检测结果默认保存到`./inference_results`文件夹里面,结果文件的名称前缀为'e2e_res'。结果示例如下:
![](../imgs_results/e2e_res_img_10_pgnet.jpg)
#### (2). 弯曲文本检测模型(Total-Text)
和四边形文本检测模型共用一个推理模型
**PGNet端到端模型推理,需要设置参数`--e2e_algorithm="PGNet"`,同时,还需要增加参数`--e2e_pgnet_polygon=True`,**可以执行如下命令:
```
python3.7 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
```
可视化文本端到端结果默认保存到`./inference_results`文件夹里面,结果文件的名称前缀为'e2e_res'。结果示例如下:
![](../imgs_results/e2e_res_img623_pgnet.jpg)
<a name="方向分类模型推理"></a> <a name="方向分类模型推理"></a>
## 、方向分类模型推理 ## 、方向分类模型推理
下面将介绍方向分类模型推理。 下面将介绍方向分类模型推理。
...@@ -418,7 +385,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982] ...@@ -418,7 +385,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
``` ```
<a name="文本检测、方向分类和文字识别串联推理"></a> <a name="文本检测、方向分类和文字识别串联推理"></a>
## 、文本检测、方向分类和文字识别串联推理 ## 、文本检测、方向分类和文字识别串联推理
<a name="超轻量中文OCR模型推理"></a> <a name="超轻量中文OCR模型推理"></a>
### 1. 超轻量中文OCR模型推理 ### 1. 超轻量中文OCR模型推理
......
...@@ -200,18 +200,16 @@ class E2ELabelEncode(BaseRecLabelEncode): ...@@ -200,18 +200,16 @@ class E2ELabelEncode(BaseRecLabelEncode):
self.pad_num = len(self.dict) # the length to pad self.pad_num = len(self.dict) # the length to pad
def __call__(self, data): def __call__(self, data):
text_label_index_list, temp_text = [], []
texts = data['strs'] texts = data['strs']
temp_texts = []
for text in texts: for text in texts:
text = text.lower() text = text.lower()
temp_text = [] text = self.encode(text)
for c_ in text: if text is None:
if c_ in self.dict: return None
temp_text.append(self.dict[c_]) text = text + [self.pad_num] * (self.max_text_len - len(text))
temp_text = temp_text + [self.pad_num] * (self.max_text_len - temp_texts.append(text)
len(temp_text)) data['strs'] = np.array(temp_texts)
text_label_index_list.append(temp_text)
data['strs'] = np.array(text_label_index_list)
return data return data
......
...@@ -19,7 +19,7 @@ from __future__ import print_function ...@@ -19,7 +19,7 @@ from __future__ import print_function
__all__ = ['E2EMetric'] __all__ = ['E2EMetric']
from ppocr.utils.e2e_metric.Deteval import get_socre, combine_results from ppocr.utils.e2e_metric.Deteval import get_socre, combine_results
from ppocr.utils.e2e_utils.extract_textpoint import get_dict from ppocr.utils.e2e_utils.extract_textpoint_slow import get_dict
class E2EMetric(object): class E2EMetric(object):
......
...@@ -22,10 +22,7 @@ import sys ...@@ -22,10 +22,7 @@ import sys
__dir__ = os.path.dirname(__file__) __dir__ = os.path.dirname(__file__)
sys.path.append(__dir__) sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__, '..')) sys.path.append(os.path.join(__dir__, '..'))
from ppocr.utils.e2e_utils.pgnet_pp_utils import PGNet_PostProcess
from ppocr.utils.e2e_utils.extract_textpoint import *
from ppocr.utils.e2e_utils.visual import *
import paddle
class PGPostProcess(object): class PGPostProcess(object):
...@@ -33,10 +30,12 @@ class PGPostProcess(object): ...@@ -33,10 +30,12 @@ class PGPostProcess(object):
The post process for PGNet. The post process for PGNet.
""" """
def __init__(self, character_dict_path, valid_set, score_thresh, **kwargs): def __init__(self, character_dict_path, valid_set, score_thresh, mode,
self.Lexicon_Table = get_dict(character_dict_path) **kwargs):
self.character_dict_path = character_dict_path
self.valid_set = valid_set self.valid_set = valid_set
self.score_thresh = score_thresh self.score_thresh = score_thresh
self.mode = mode
# c++ la-nms is faster, but only support python 3.5 # c++ la-nms is faster, but only support python 3.5
self.is_python35 = False self.is_python35 = False
...@@ -44,113 +43,10 @@ class PGPostProcess(object): ...@@ -44,113 +43,10 @@ class PGPostProcess(object):
self.is_python35 = True self.is_python35 = True
def __call__(self, outs_dict, shape_list): def __call__(self, outs_dict, shape_list):
p_score = outs_dict['f_score'] post = PGNet_PostProcess(self.character_dict_path, self.valid_set,
p_border = outs_dict['f_border'] self.score_thresh, outs_dict, shape_list)
p_char = outs_dict['f_char'] if self.mode == 'fast':
p_direction = outs_dict['f_direction'] data = post.pg_postprocess_fast()
if isinstance(p_score, paddle.Tensor):
p_score = p_score[0].numpy()
p_border = p_border[0].numpy()
p_direction = p_direction[0].numpy()
p_char = p_char[0].numpy()
else: else:
p_score = p_score[0] data = post.pg_postprocess_slow()
p_border = p_border[0]
p_direction = p_direction[0]
p_char = p_char[0]
src_h, src_w, ratio_h, ratio_w = shape_list[0]
is_curved = self.valid_set == "totaltext"
instance_yxs_list = generate_pivot_list(
p_score,
p_char,
p_direction,
score_thresh=self.score_thresh,
is_backbone=True,
is_curved=is_curved)
p_char = paddle.to_tensor(np.expand_dims(p_char, axis=0))
char_seq_idx_set = []
for i in range(len(instance_yxs_list)):
gather_info_lod = paddle.to_tensor(instance_yxs_list[i])
f_char_map = paddle.transpose(p_char, [0, 2, 3, 1])
feature_seq = paddle.gather_nd(f_char_map, gather_info_lod)
feature_seq = np.expand_dims(feature_seq.numpy(), axis=0)
feature_len = [len(feature_seq[0])]
featyre_seq = paddle.to_tensor(feature_seq)
feature_len = np.array([feature_len]).astype(np.int64)
length = paddle.to_tensor(feature_len)
seq_pred = paddle.fluid.layers.ctc_greedy_decoder(
input=featyre_seq, blank=36, input_length=length)
seq_pred_str = seq_pred[0].numpy().tolist()[0]
seq_len = seq_pred[1].numpy()[0][0]
temp_t = []
for c in seq_pred_str[:seq_len]:
temp_t.append(c)
char_seq_idx_set.append(temp_t)
seq_strs = []
for char_idx_set in char_seq_idx_set:
pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
seq_strs.append(pr_str)
poly_list = []
keep_str_list = []
all_point_list = []
all_point_pair_list = []
for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
if len(yx_center_line) == 1:
yx_center_line.append(yx_center_line[-1])
offset_expand = 1.0
if self.valid_set == 'totaltext':
offset_expand = 1.2
point_pair_list = []
for batch_id, y, x in yx_center_line:
offset = p_border[:, y, x].reshape(2, 2)
if offset_expand != 1.0:
offset_length = np.linalg.norm(
offset, axis=1, keepdims=True)
expand_length = np.clip(
offset_length * (offset_expand - 1),
a_min=0.5,
a_max=3.0)
offset_detal = offset / offset_length * expand_length
offset = offset + offset_detal
ori_yx = np.array([y, x], dtype=np.float32)
point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
[ratio_w, ratio_h]).reshape(-1, 2)
point_pair_list.append(point_pair)
all_point_list.append([
int(round(x * 4.0 / ratio_w)),
int(round(y * 4.0 / ratio_h))
])
all_point_pair_list.append(point_pair.round().astype(np.int32)
.tolist())
detected_poly, pair_length_info = point_pair2poly(point_pair_list)
detected_poly = expand_poly_along_width(
detected_poly, shrink_ratio_of_width=0.2)
detected_poly[:, 0] = np.clip(
detected_poly[:, 0], a_min=0, a_max=src_w)
detected_poly[:, 1] = np.clip(
detected_poly[:, 1], a_min=0, a_max=src_h)
if len(keep_str) < 2:
continue
keep_str_list.append(keep_str)
detected_poly = np.round(detected_poly).astype('int32')
if self.valid_set == 'partvgg':
middle_point = len(detected_poly) // 2
detected_poly = detected_poly[
[0, middle_point - 1, middle_point, -1], :]
poly_list.append(detected_poly)
elif self.valid_set == 'totaltext':
poly_list.append(detected_poly)
else:
print('--> Not supported format.')
exit(-1)
data = {
'points': poly_list,
'strs': keep_str_list,
}
return data return data
# 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.
"""Contains various CTC decoders."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import math
import numpy as np
from itertools import groupby
from cv2.ximgproc import thinning as thin
def get_dict(character_dict_path):
character_str = ""
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
for line in lines:
line = line.decode('utf-8').strip("\n").strip("\r\n")
character_str += line
dict_character = list(character_str)
return dict_character
def softmax(logits):
"""
logits: N x d
"""
max_value = np.max(logits, axis=1, keepdims=True)
exp = np.exp(logits - max_value)
exp_sum = np.sum(exp, axis=1, keepdims=True)
dist = exp / exp_sum
return dist
def get_keep_pos_idxs(labels, remove_blank=None):
"""
Remove duplicate and get pos idxs of keep items.
The value of keep_blank should be [None, 95].
"""
duplicate_len_list = []
keep_pos_idx_list = []
keep_char_idx_list = []
for k, v_ in groupby(labels):
current_len = len(list(v_))
if k != remove_blank:
current_idx = int(sum(duplicate_len_list) + current_len // 2)
keep_pos_idx_list.append(current_idx)
keep_char_idx_list.append(k)
duplicate_len_list.append(current_len)
return keep_char_idx_list, keep_pos_idx_list
def remove_blank(labels, blank=0):
new_labels = [x for x in labels if x != blank]
return new_labels
def insert_blank(labels, blank=0):
new_labels = [blank]
for l in labels:
new_labels += [l, blank]
return new_labels
def ctc_greedy_decoder(probs_seq, blank=95, keep_blank_in_idxs=True):
"""
CTC greedy (best path) decoder.
"""
raw_str = np.argmax(np.array(probs_seq), axis=1)
remove_blank_in_pos = None if keep_blank_in_idxs else blank
dedup_str, keep_idx_list = get_keep_pos_idxs(
raw_str, remove_blank=remove_blank_in_pos)
dst_str = remove_blank(dedup_str, blank=blank)
return dst_str, keep_idx_list
def instance_ctc_greedy_decoder(gather_info, logits_map, pts_num=4):
_, _, C = logits_map.shape
ys, xs = zip(*gather_info)
logits_seq = logits_map[list(ys), list(xs)]
probs_seq = logits_seq
labels = np.argmax(probs_seq, axis=1)
dst_str = [k for k, v_ in groupby(labels) if k != C - 1]
detal = len(gather_info) // (pts_num - 1)
keep_idx_list = [0] + [detal * (i + 1) for i in range(pts_num - 2)] + [-1]
keep_gather_list = [gather_info[idx] for idx in keep_idx_list]
return dst_str, keep_gather_list
def ctc_decoder_for_image(gather_info_list,
logits_map,
Lexicon_Table,
pts_num=6):
"""
CTC decoder using multiple processes.
"""
decoder_str = []
decoder_xys = []
for gather_info in gather_info_list:
if len(gather_info) < pts_num:
continue
dst_str, xys_list = instance_ctc_greedy_decoder(
gather_info, logits_map, pts_num=pts_num)
dst_str_readable = ''.join([Lexicon_Table[idx] for idx in dst_str])
if len(dst_str_readable) < 2:
continue
decoder_str.append(dst_str_readable)
decoder_xys.append(xys_list)
return decoder_str, decoder_xys
def sort_with_direction(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
def sort_part_with_direction(pos_list, point_direction):
pos_list = np.array(pos_list).reshape(-1, 2)
point_direction = np.array(point_direction).reshape(-1, 2)
average_direction = np.mean(point_direction, axis=0, keepdims=True)
pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
sorted_list = pos_list[np.argsort(pos_proj_leng)].tolist()
sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
return sorted_list, sorted_direction
pos_list = np.array(pos_list).reshape(-1, 2)
point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]] # x, y
point_direction = point_direction[:, ::-1] # x, y -> y, x
sorted_point, sorted_direction = sort_part_with_direction(pos_list,
point_direction)
point_num = len(sorted_point)
if point_num >= 16:
middle_num = point_num // 2
first_part_point = sorted_point[:middle_num]
first_point_direction = sorted_direction[:middle_num]
sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
first_part_point, first_point_direction)
last_part_point = sorted_point[middle_num:]
last_point_direction = sorted_direction[middle_num:]
sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
last_part_point, last_point_direction)
sorted_point = sorted_fist_part_point + sorted_last_part_point
sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
return sorted_point, np.array(sorted_direction)
def add_id(pos_list, image_id=0):
"""
Add id for gather feature, for inference.
"""
new_list = []
for item in pos_list:
new_list.append((image_id, item[0], item[1]))
return new_list
def sort_and_expand_with_direction(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
h, w, _ = f_direction.shape
sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
point_num = len(sorted_list)
sub_direction_len = max(point_num // 3, 2)
left_direction = point_direction[:sub_direction_len, :]
right_dirction = point_direction[point_num - sub_direction_len:, :]
left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
left_average_len = np.linalg.norm(left_average_direction)
left_start = np.array(sorted_list[0])
left_step = left_average_direction / (left_average_len + 1e-6)
right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
right_average_len = np.linalg.norm(right_average_direction)
right_step = right_average_direction / (right_average_len + 1e-6)
right_start = np.array(sorted_list[-1])
append_num = max(
int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
left_list = []
right_list = []
for i in range(append_num):
ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
'int32').tolist()
if ly < h and lx < w and (ly, lx) not in left_list:
left_list.append((ly, lx))
ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
'int32').tolist()
if ry < h and rx < w and (ry, rx) not in right_list:
right_list.append((ry, rx))
all_list = left_list[::-1] + sorted_list + right_list
return all_list
def sort_and_expand_with_direction_v2(pos_list, f_direction, binary_tcl_map):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
binary_tcl_map: h x w
"""
h, w, _ = f_direction.shape
sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
point_num = len(sorted_list)
sub_direction_len = max(point_num // 3, 2)
left_direction = point_direction[:sub_direction_len, :]
right_dirction = point_direction[point_num - sub_direction_len:, :]
left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
left_average_len = np.linalg.norm(left_average_direction)
left_start = np.array(sorted_list[0])
left_step = left_average_direction / (left_average_len + 1e-6)
right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
right_average_len = np.linalg.norm(right_average_direction)
right_step = right_average_direction / (right_average_len + 1e-6)
right_start = np.array(sorted_list[-1])
append_num = max(
int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
max_append_num = 2 * append_num
left_list = []
right_list = []
for i in range(max_append_num):
ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
'int32').tolist()
if ly < h and lx < w and (ly, lx) not in left_list:
if binary_tcl_map[ly, lx] > 0.5:
left_list.append((ly, lx))
else:
break
for i in range(max_append_num):
ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
'int32').tolist()
if ry < h and rx < w and (ry, rx) not in right_list:
if binary_tcl_map[ry, rx] > 0.5:
right_list.append((ry, rx))
else:
break
all_list = left_list[::-1] + sorted_list + right_list
return all_list
def point_pair2poly(point_pair_list):
"""
Transfer vertical point_pairs into poly point in clockwise.
"""
point_num = len(point_pair_list) * 2
point_list = [0] * point_num
for idx, point_pair in enumerate(point_pair_list):
point_list[idx] = point_pair[0]
point_list[point_num - 1 - idx] = point_pair[1]
return np.array(point_list).reshape(-1, 2)
def shrink_quad_along_width(quad, begin_width_ratio=0., end_width_ratio=1.):
ratio_pair = np.array(
[[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
def expand_poly_along_width(poly, shrink_ratio_of_width=0.3):
"""
expand poly along width.
"""
point_num = poly.shape[0]
left_quad = np.array(
[poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
(np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
left_quad_expand = shrink_quad_along_width(left_quad, left_ratio, 1.0)
right_quad = np.array(
[
poly[point_num // 2 - 2], poly[point_num // 2 - 1],
poly[point_num // 2], poly[point_num // 2 + 1]
],
dtype=np.float32)
right_ratio = 1.0 + shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
(np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
right_quad_expand = shrink_quad_along_width(right_quad, 0.0, right_ratio)
poly[0] = left_quad_expand[0]
poly[-1] = left_quad_expand[-1]
poly[point_num // 2 - 1] = right_quad_expand[1]
poly[point_num // 2] = right_quad_expand[2]
return poly
def restore_poly(instance_yxs_list, seq_strs, p_border, ratio_w, ratio_h, src_w,
src_h, valid_set):
poly_list = []
keep_str_list = []
for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
if len(keep_str) < 2:
print('--> too short, {}'.format(keep_str))
continue
offset_expand = 1.0
if valid_set == 'totaltext':
offset_expand = 1.2
point_pair_list = []
for y, x in yx_center_line:
offset = p_border[:, y, x].reshape(2, 2) * offset_expand
ori_yx = np.array([y, x], dtype=np.float32)
point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
[ratio_w, ratio_h]).reshape(-1, 2)
point_pair_list.append(point_pair)
detected_poly = point_pair2poly(point_pair_list)
detected_poly = expand_poly_along_width(
detected_poly, shrink_ratio_of_width=0.2)
detected_poly[:, 0] = np.clip(detected_poly[:, 0], a_min=0, a_max=src_w)
detected_poly[:, 1] = np.clip(detected_poly[:, 1], a_min=0, a_max=src_h)
keep_str_list.append(keep_str)
if valid_set == 'partvgg':
middle_point = len(detected_poly) // 2
detected_poly = detected_poly[
[0, middle_point - 1, middle_point, -1], :]
poly_list.append(detected_poly)
elif valid_set == 'totaltext':
poly_list.append(detected_poly)
else:
print('--> Not supported format.')
exit(-1)
return poly_list, keep_str_list
def generate_pivot_list_fast(p_score,
p_char_maps,
f_direction,
Lexicon_Table,
score_thresh=0.5):
"""
return center point and end point of TCL instance; filter with the char maps;
"""
p_score = p_score[0]
f_direction = f_direction.transpose(1, 2, 0)
ret, p_tcl_map = cv2.threshold(p_score, score_thresh, 255,
cv2.THRESH_BINARY)
skeleton_map = thin(p_tcl_map.astype('uint8'))
instance_count, instance_label_map = cv2.connectedComponents(
skeleton_map, connectivity=8)
# get TCL Instance
all_pos_yxs = []
if instance_count > 0:
for instance_id in range(1, instance_count):
pos_list = []
ys, xs = np.where(instance_label_map == instance_id)
pos_list = list(zip(ys, xs))
if len(pos_list) < 3:
continue
pos_list_sorted = sort_and_expand_with_direction_v2(
pos_list, f_direction, p_tcl_map)
all_pos_yxs.append(pos_list_sorted)
p_char_maps = p_char_maps.transpose([1, 2, 0])
decoded_str, keep_yxs_list = ctc_decoder_for_image(
all_pos_yxs, logits_map=p_char_maps, Lexicon_Table=Lexicon_Table)
return keep_yxs_list, decoded_str
def extract_main_direction(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
pos_list = np.array(pos_list)
point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]
point_direction = point_direction[:, ::-1] # x, y -> y, x
average_direction = np.mean(point_direction, axis=0, keepdims=True)
average_direction = average_direction / (
np.linalg.norm(average_direction) + 1e-6)
return average_direction
def sort_by_direction_with_image_id_deprecated(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[id, y, x], [id, y, x], [id, y, x] ...]
"""
pos_list_full = np.array(pos_list).reshape(-1, 3)
pos_list = pos_list_full[:, 1:]
point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]] # x, y
point_direction = point_direction[:, ::-1] # x, y -> y, x
average_direction = np.mean(point_direction, axis=0, keepdims=True)
pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
return sorted_list
def sort_by_direction_with_image_id(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
def sort_part_with_direction(pos_list_full, point_direction):
pos_list_full = np.array(pos_list_full).reshape(-1, 3)
pos_list = pos_list_full[:, 1:]
point_direction = np.array(point_direction).reshape(-1, 2)
average_direction = np.mean(point_direction, axis=0, keepdims=True)
pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
return sorted_list, sorted_direction
pos_list = np.array(pos_list).reshape(-1, 3)
point_direction = f_direction[pos_list[:, 1], pos_list[:, 2]] # x, y
point_direction = point_direction[:, ::-1] # x, y -> y, x
sorted_point, sorted_direction = sort_part_with_direction(pos_list,
point_direction)
point_num = len(sorted_point)
if point_num >= 16:
middle_num = point_num // 2
first_part_point = sorted_point[:middle_num]
first_point_direction = sorted_direction[:middle_num]
sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
first_part_point, first_point_direction)
last_part_point = sorted_point[middle_num:]
last_point_direction = sorted_direction[middle_num:]
sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
last_part_point, last_point_direction)
sorted_point = sorted_fist_part_point + sorted_last_part_point
sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
return sorted_point
...@@ -21,7 +21,7 @@ import math ...@@ -21,7 +21,7 @@ import math
import numpy as np import numpy as np
from itertools import groupby from itertools import groupby
from skimage.morphology._skeletonize import thin from cv2.ximgproc import thinning as thin
def get_dict(character_dict_path): def get_dict(character_dict_path):
...@@ -399,13 +399,13 @@ def generate_pivot_list_horizontal(p_score, ...@@ -399,13 +399,13 @@ def generate_pivot_list_horizontal(p_score,
return center_pos_yxs, end_points_yxs return center_pos_yxs, end_points_yxs
def generate_pivot_list(p_score, def generate_pivot_list_slow(p_score,
p_char_maps, p_char_maps,
f_direction, f_direction,
score_thresh=0.5, score_thresh=0.5,
is_backbone=False, is_backbone=False,
is_curved=True, is_curved=True,
image_id=0): image_id=0):
""" """
Warp all the function together. Warp all the function together.
""" """
......
# 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 paddle
from extract_textpoint_slow import *
from extract_textpoint_fast import *
class PGNet_PostProcess(object):
# two different post-process
def __init__(self, character_dict_path, valid_set, score_thresh, outs_dict,
shape_list):
self.Lexicon_Table = get_dict(character_dict_path)
self.valid_set = valid_set
self.score_thresh = score_thresh
self.outs_dict = outs_dict
self.shape_list = shape_list
def pg_postprocess_fast(self):
p_score = self.outs_dict['f_score']
p_border = self.outs_dict['f_border']
p_char = self.outs_dict['f_char']
p_direction = self.outs_dict['f_direction']
if isinstance(p_score, paddle.Tensor):
p_score = p_score[0].numpy()
p_border = p_border[0].numpy()
p_direction = p_direction[0].numpy()
p_char = p_char[0].numpy()
else:
p_score = p_score[0]
p_border = p_border[0]
p_direction = p_direction[0]
p_char = p_char[0]
src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
instance_yxs_list, seq_strs = generate_pivot_list_fast(
p_score,
p_char,
p_direction,
self.Lexicon_Table,
score_thresh=self.score_thresh)
poly_list, keep_str_list = restore_poly(instance_yxs_list, seq_strs,
p_border, ratio_w, ratio_h,
src_w, src_h, self.valid_set)
data = {
'points': poly_list,
'strs': keep_str_list,
}
return data
def pg_postprocess_slow(self):
p_score = self.outs_dict['f_score']
p_border = self.outs_dict['f_border']
p_char = self.outs_dict['f_char']
p_direction = self.outs_dict['f_direction']
if isinstance(p_score, paddle.Tensor):
p_score = p_score[0].numpy()
p_border = p_border[0].numpy()
p_direction = p_direction[0].numpy()
p_char = p_char[0].numpy()
else:
p_score = p_score[0]
p_border = p_border[0]
p_direction = p_direction[0]
p_char = p_char[0]
src_h, src_w, ratio_h, ratio_w = self.shape_list[0]
is_curved = self.valid_set == "totaltext"
instance_yxs_list = generate_pivot_list_slow(
p_score,
p_char,
p_direction,
score_thresh=self.score_thresh,
is_backbone=True,
is_curved=is_curved)
p_char = paddle.to_tensor(np.expand_dims(p_char, axis=0))
char_seq_idx_set = []
for i in range(len(instance_yxs_list)):
gather_info_lod = paddle.to_tensor(instance_yxs_list[i])
f_char_map = paddle.transpose(p_char, [0, 2, 3, 1])
feature_seq = paddle.gather_nd(f_char_map, gather_info_lod)
feature_seq = np.expand_dims(feature_seq.numpy(), axis=0)
feature_len = [len(feature_seq[0])]
featyre_seq = paddle.to_tensor(feature_seq)
feature_len = np.array([feature_len]).astype(np.int64)
length = paddle.to_tensor(feature_len)
seq_pred = paddle.fluid.layers.ctc_greedy_decoder(
input=featyre_seq, blank=36, input_length=length)
seq_pred_str = seq_pred[0].numpy().tolist()[0]
seq_len = seq_pred[1].numpy()[0][0]
temp_t = []
for c in seq_pred_str[:seq_len]:
temp_t.append(c)
char_seq_idx_set.append(temp_t)
seq_strs = []
for char_idx_set in char_seq_idx_set:
pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
seq_strs.append(pr_str)
poly_list = []
keep_str_list = []
all_point_list = []
all_point_pair_list = []
for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
if len(yx_center_line) == 1:
yx_center_line.append(yx_center_line[-1])
offset_expand = 1.0
if self.valid_set == 'totaltext':
offset_expand = 1.2
point_pair_list = []
for batch_id, y, x in yx_center_line:
offset = p_border[:, y, x].reshape(2, 2)
if offset_expand != 1.0:
offset_length = np.linalg.norm(
offset, axis=1, keepdims=True)
expand_length = np.clip(
offset_length * (offset_expand - 1),
a_min=0.5,
a_max=3.0)
offset_detal = offset / offset_length * expand_length
offset = offset + offset_detal
ori_yx = np.array([y, x], dtype=np.float32)
point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
[ratio_w, ratio_h]).reshape(-1, 2)
point_pair_list.append(point_pair)
all_point_list.append([
int(round(x * 4.0 / ratio_w)),
int(round(y * 4.0 / ratio_h))
])
all_point_pair_list.append(point_pair.round().astype(np.int32)
.tolist())
detected_poly, pair_length_info = point_pair2poly(point_pair_list)
detected_poly = expand_poly_along_width(
detected_poly, shrink_ratio_of_width=0.2)
detected_poly[:, 0] = np.clip(
detected_poly[:, 0], a_min=0, a_max=src_w)
detected_poly[:, 1] = np.clip(
detected_poly[:, 1], a_min=0, a_max=src_h)
if len(keep_str) < 2:
continue
keep_str_list.append(keep_str)
detected_poly = np.round(detected_poly).astype('int32')
if self.valid_set == 'partvgg':
middle_point = len(detected_poly) // 2
detected_poly = detected_poly[
[0, middle_point - 1, middle_point, -1], :]
poly_list.append(detected_poly)
elif self.valid_set == 'totaltext':
poly_list.append(detected_poly)
else:
print('--> Not supported format.')
exit(-1)
data = {
'points': poly_list,
'strs': keep_str_list,
}
return data
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