Skip to content

P
PaddleOCR

PaddlePaddle / PaddleOCR
大约 1 年 前同步成功

通知 1528
Star 32962
Fork 6643
P
PaddleOCR
提交 a0d1f923 编写于 作者: J Jethong
浏览文件
操作

add different post process

上级 0cd48c35
展开全部 显示空白变更内容
内联 并排
Showing with 516 addition and 434 deletion
configs/e2e/e2e_r50_vd_pg.yml
浏览文件 @ a0d1f923
......@@ -11,7 +11,7 @@ Global:
# from static branch, load_static_weights must be set as True.
# 2. If you want to finetune the pretrained models we provide in the docs,
# you should set load_static_weights as False.
load_static_weights: True
load_static_weights: False
cal_metric_during_train: False
pretrained_model:
checkpoints:
......@@ -94,7 +94,7 @@ Eval:
label_file_list: [./train_data/total_text/test/]
transforms:
- DecodeImage: # load image
img_mode: BGR
img_mode: RGB
channel_first: False
- E2ELabelEncode:
- E2EResizeForTest:
......
ppocr/data/imaug/label_ops.py
浏览文件 @ a0d1f923
......@@ -200,16 +200,18 @@ class E2ELabelEncode(BaseRecLabelEncode):
self.pad_num = len(self.dict) # the length to pad
def __call__(self, data):
text_label_index_list, temp_text = [], []
texts = data['strs']
temp_texts = []
for text in texts:
text = text.lower()
text = self.encode(text)
if text is None:
return None
text = text + [self.pad_num] * (self.max_text_len - len(text))
temp_texts.append(text)
data['strs'] = np.array(temp_texts)
temp_text = []
for c_ in text:
if c_ in self.dict:
temp_text.append(self.dict[c_])
temp_text = temp_text + [self.pad_num] * (self.max_text_len -
len(temp_text))
text_label_index_list.append(temp_text)
data['strs'] = np.array(text_label_index_list)
return data
......
ppocr/data/pgnet_dataset.py
浏览文件 @ a0d1f923
......@@ -24,6 +24,7 @@ class PGDataSet(Dataset):
self.logger = logger
self.seed = seed
self.mode = mode
global_config = config['Global']
dataset_config = config[mode]['dataset']
loader_config = config[mode]['loader']
......@@ -62,10 +63,13 @@ class PGDataSet(Dataset):
with open(poly_txt_path) as f:
for line in f.readlines():
poly_str, txt = line.strip().split('\t')
poly = map(float, poly_str.split(','))
poly = list(map(float, poly_str.split(',')))
if self.mode.lower() == "eval":
while len(poly) < 100:
poly.append(-1)
text_polys.append(
np.array(
list(poly), dtype=np.float32).reshape(-1, 2))
poly, dtype=np.float32).reshape(-1, 2))
txts.append(txt)
txt_tags.append(txt == '###')
......@@ -135,6 +139,10 @@ class PGDataSet(Dataset):
try:
if self.data_format == 'icdar':
im_path = os.path.join(data_path, 'rgb', data_line)
if self.mode.lower() == "eval":
poly_path = os.path.join(data_path, 'poly_gt',
data_line.split('.')[0] + '.txt')
else:
poly_path = os.path.join(data_path, 'poly',
data_line.split('.')[0] + '.txt')
text_polys, text_tags, text_strs = self.extract_polys(poly_path)
......
ppocr/metrics/e2e_metric.py
浏览文件 @ a0d1f923
......@@ -33,10 +33,20 @@ class E2EMetric(object):
self.reset()
def __call__(self, preds, batch, **kwargs):
gt_polyons_batch = batch[2]
temp_gt_polyons_batch = batch[2]
temp_gt_strs_batch = batch[3]
ignore_tags_batch = batch[4]
gt_polyons_batch = []
gt_strs_batch = []
temp_gt_polyons_batch = temp_gt_polyons_batch[0].tolist()
for temp_list in temp_gt_polyons_batch:
t = []
for index in temp_list:
if index[0] != -1 and index[1] != -1:
t.append(index)
gt_polyons_batch.append(t)
temp_gt_strs_batch = temp_gt_strs_batch[0].tolist()
for temp_list in temp_gt_strs_batch:
t = ""
......@@ -46,7 +56,7 @@ class E2EMetric(object):
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,
......
ppocr/postprocess/pg_postprocess.py
浏览文件 @ a0d1f923
......@@ -23,7 +23,8 @@ __dir__ = os.path.dirname(__file__)
sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__, '..'))
from ppocr.utils.e2e_utils.extract_textpoint import get_dict, generate_pivot_list, restore_poly
from ppocr.utils.e2e_utils.extract_textpoint import *
from ppocr.utils.e2e_utils.visual import *
import paddle
......@@ -37,6 +38,11 @@ class PGPostProcess(object):
self.valid_set = valid_set
self.score_thresh = score_thresh
# c++ la-nms is faster, but only support python 3.5
self.is_python35 = False
if sys.version_info.major == 3 and sys.version_info.minor == 5:
self.is_python35 = True
def __call__(self, outs_dict, shape_list):
p_score = outs_dict['f_score']
p_border = outs_dict['f_border']
......@@ -52,17 +58,96 @@ class PGPostProcess(object):
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]
instance_yxs_list, seq_strs = generate_pivot_list(
is_curved = self.valid_set == "totaltext"
instance_yxs_list = generate_pivot_list(
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)
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)
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,
......
ppocr/utils/e2e_metric/Deteval.py
浏览文件 @ a0d1f923
......@@ -35,7 +35,7 @@ def get_socre(gt_dict, pred_dict):
gt = []
n = len(gt_dict)
for i in range(n):
points = gt_dict[i]['points'].tolist()
points = gt_dict[i]['points']
h = len(points)
text = gt_dict[i]['text']
xx = [
......@@ -51,7 +51,7 @@ def get_socre(gt_dict, pred_dict):
t_y.append(points[j][1])
xx[1] = np.array([t_x], dtype='int16')
xx[3] = np.array([t_y], dtype='int16')
if text != "":
if text != "" and "#" not in text:
xx[4] = np.array([text], dtype='U{}'.format(len(text)))
xx[5] = np.array(['c'], dtype='<U1')
gt.append(xx)
......@@ -89,17 +89,10 @@ def get_socre(gt_dict, pred_dict):
area(det_x, det_y)), 2)
##############################Initialization###################################
global_tp = 0
global_fp = 0
global_fn = 0
global_sigma = []
global_tau = []
tr = 0.7
tp = 0.6
fsc_k = 0.8
k = 2
global_pred_str = []
global_gt_str = []
# global_sigma = []
# global_tau = []
# global_pred_str = []
# global_gt_str = []
###############################################################################
for input_id in range(allInputs):
......@@ -147,281 +140,16 @@ def get_socre(gt_dict, pred_dict):
local_pred_str[det_id] = pred_seq_str
local_gt_str[gt_id] = gt_seq_str
global_sigma.append(local_sigma_table)
global_tau.append(local_tau_table)
global_pred_str.append(local_pred_str)
global_gt_str.append(local_gt_str)
global_accumulative_recall = 0
global_accumulative_precision = 0
total_num_gt = 0
total_num_det = 0
hit_str_count = 0
hit_count = 0
def one_to_one(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idy):
hit_str_num = 0
for gt_id in range(num_gt):
gt_matching_qualified_sigma_candidates = np.where(
local_sigma_table[gt_id, :] > tr)
gt_matching_num_qualified_sigma_candidates = gt_matching_qualified_sigma_candidates[
0].shape[0]
gt_matching_qualified_tau_candidates = np.where(
local_tau_table[gt_id, :] > tp)
gt_matching_num_qualified_tau_candidates = gt_matching_qualified_tau_candidates[
0].shape[0]
det_matching_qualified_sigma_candidates = np.where(
local_sigma_table[:, gt_matching_qualified_sigma_candidates[0]]
> tr)
det_matching_num_qualified_sigma_candidates = det_matching_qualified_sigma_candidates[
0].shape[0]
det_matching_qualified_tau_candidates = np.where(
local_tau_table[:, gt_matching_qualified_tau_candidates[0]] >
tp)
det_matching_num_qualified_tau_candidates = det_matching_qualified_tau_candidates[
0].shape[0]
if (gt_matching_num_qualified_sigma_candidates == 1) and (gt_matching_num_qualified_tau_candidates == 1) and \
(det_matching_num_qualified_sigma_candidates == 1) and (
det_matching_num_qualified_tau_candidates == 1):
global_accumulative_recall = global_accumulative_recall + 1.0
global_accumulative_precision = global_accumulative_precision + 1.0
local_accumulative_recall = local_accumulative_recall + 1.0
local_accumulative_precision = local_accumulative_precision + 1.0
gt_flag[0, gt_id] = 1
matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
# recg start
gt_str_cur = global_gt_str[idy][gt_id]
pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[
0]]
if pred_str_cur == gt_str_cur:
hit_str_num += 1
else:
if pred_str_cur.lower() == gt_str_cur.lower():
hit_str_num += 1
# recg end
det_flag[0, matched_det_id] = 1
return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
def one_to_many(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idy):
hit_str_num = 0
for gt_id in range(num_gt):
# skip the following if the groundtruth was matched
if gt_flag[0, gt_id] > 0:
continue
non_zero_in_sigma = np.where(local_sigma_table[gt_id, :] > 0)
num_non_zero_in_sigma = non_zero_in_sigma[0].shape[0]
if num_non_zero_in_sigma >= k:
####search for all detections that overlaps with this groundtruth
qualified_tau_candidates = np.where((local_tau_table[
gt_id, :] >= tp) & (det_flag[0, :] == 0))
num_qualified_tau_candidates = qualified_tau_candidates[
0].shape[0]
if num_qualified_tau_candidates == 1:
if ((local_tau_table[gt_id, qualified_tau_candidates] >= tp)
and
(local_sigma_table[gt_id, qualified_tau_candidates] >=
tr)):
# became an one-to-one case
global_accumulative_recall = global_accumulative_recall + 1.0
global_accumulative_precision = global_accumulative_precision + 1.0
local_accumulative_recall = local_accumulative_recall + 1.0
local_accumulative_precision = local_accumulative_precision + 1.0
gt_flag[0, gt_id] = 1
det_flag[0, qualified_tau_candidates] = 1
# recg start
gt_str_cur = global_gt_str[idy][gt_id]
pred_str_cur = global_pred_str[idy][
qualified_tau_candidates[0].tolist()[0]]
if pred_str_cur == gt_str_cur:
hit_str_num += 1
else:
if pred_str_cur.lower() == gt_str_cur.lower():
hit_str_num += 1
# recg end
elif (np.sum(local_sigma_table[gt_id, qualified_tau_candidates])
>= tr):
gt_flag[0, gt_id] = 1
det_flag[0, qualified_tau_candidates] = 1
# recg start
gt_str_cur = global_gt_str[idy][gt_id]
pred_str_cur = global_pred_str[idy][
qualified_tau_candidates[0].tolist()[0]]
if pred_str_cur == gt_str_cur:
hit_str_num += 1
else:
if pred_str_cur.lower() == gt_str_cur.lower():
hit_str_num += 1
# recg end
global_accumulative_recall = global_accumulative_recall + fsc_k
global_accumulative_precision = global_accumulative_precision + num_qualified_tau_candidates * fsc_k
local_accumulative_recall = local_accumulative_recall + fsc_k
local_accumulative_precision = local_accumulative_precision + num_qualified_tau_candidates * fsc_k
return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
def many_to_one(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idy):
hit_str_num = 0
for det_id in range(num_det):
# skip the following if the detection was matched
if det_flag[0, det_id] > 0:
continue
non_zero_in_tau = np.where(local_tau_table[:, det_id] > 0)
num_non_zero_in_tau = non_zero_in_tau[0].shape[0]
if num_non_zero_in_tau >= k:
####search for all detections that overlaps with this groundtruth
qualified_sigma_candidates = np.where((
local_sigma_table[:, det_id] >= tp) & (gt_flag[0, :] == 0))
num_qualified_sigma_candidates = qualified_sigma_candidates[
0].shape[0]
if num_qualified_sigma_candidates == 1:
if ((local_tau_table[qualified_sigma_candidates, det_id] >=
tp) and
(local_sigma_table[qualified_sigma_candidates, det_id]
>= tr)):
# became an one-to-one case
global_accumulative_recall = global_accumulative_recall + 1.0
global_accumulative_precision = global_accumulative_precision + 1.0
local_accumulative_recall = local_accumulative_recall + 1.0
local_accumulative_precision = local_accumulative_precision + 1.0
gt_flag[0, qualified_sigma_candidates] = 1
det_flag[0, det_id] = 1
# recg start
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 ele_gt_id not in global_gt_str[idy]:
continue
gt_str_cur = global_gt_str[idy][ele_gt_id]
if pred_str_cur == gt_str_cur:
hit_str_num += 1
break
else:
if pred_str_cur.lower() == gt_str_cur.lower():
hit_str_num += 1
break
# recg end
elif (np.sum(local_tau_table[qualified_sigma_candidates,
det_id]) >= tp):
det_flag[0, det_id] = 1
gt_flag[0, qualified_sigma_candidates] = 1
# recg start
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 ele_gt_id not in global_gt_str[idy]:
continue
gt_str_cur = global_gt_str[idy][ele_gt_id]
if pred_str_cur == gt_str_cur:
hit_str_num += 1
break
else:
if pred_str_cur.lower() == gt_str_cur.lower():
hit_str_num += 1
break
# recg end
global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
global_accumulative_precision = global_accumulative_precision + fsc_k
local_accumulative_recall = local_accumulative_recall + num_qualified_sigma_candidates * fsc_k
local_accumulative_precision = local_accumulative_precision + fsc_k
return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
global_sigma = local_sigma_table
global_tau = local_tau_table
global_pred_str = local_pred_str
global_gt_str = local_gt_str
single_data = {}
for idx in range(len(global_sigma)):
local_sigma_table = global_sigma[idx]
local_tau_table = global_tau[idx]
num_gt = local_sigma_table.shape[0]
num_det = local_sigma_table.shape[1]
total_num_gt = total_num_gt + num_gt
total_num_det = total_num_det + num_det
local_accumulative_recall = 0
local_accumulative_precision = 0
gt_flag = np.zeros((1, num_gt))
det_flag = np.zeros((1, num_det))
#######first check for one-to-one case##########
local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
gt_flag, det_flag, hit_str_num = one_to_one(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idx)
hit_str_count += hit_str_num
#######then check for one-to-many case##########
local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
gt_flag, det_flag, hit_str_num = one_to_many(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idx)
hit_str_count += hit_str_num
#######then check for many-to-one case##########
local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
gt_flag, det_flag, hit_str_num = many_to_one(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idx)
hit_str_count += hit_str_num
# fid = open(fid_path, 'a+')
try:
local_precision = local_accumulative_precision / num_det
except ZeroDivisionError:
local_precision = 0
try:
local_recall = local_accumulative_recall / num_gt
except ZeroDivisionError:
local_recall = 0
try:
local_f_score = 2 * local_precision * local_recall / (
local_precision + local_recall)
except ZeroDivisionError:
local_f_score = 0
single_data['sigma'] = global_sigma
single_data['global_tau'] = global_tau
single_data['global_pred_str'] = global_pred_str
single_data['global_gt_str'] = global_gt_str
single_data["recall"] = local_recall
single_data['precision'] = local_precision
single_data['f_score'] = local_f_score
return single_data
......@@ -435,10 +163,10 @@ def combine_results(all_data):
global_pred_str = []
global_gt_str = []
for data in all_data:
global_sigma.append(data['sigma'][0])
global_tau.append(data['global_tau'][0])
global_pred_str.append(data['global_pred_str'][0])
global_gt_str.append(data['global_gt_str'][0])
global_sigma.append(data['sigma'])
global_tau.append(data['global_tau'])
global_pred_str.append(data['global_pred_str'])
global_gt_str.append(data['global_gt_str'])
global_accumulative_recall = 0
global_accumulative_precision = 0
......@@ -676,6 +404,8 @@ def combine_results(all_data):
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idx)
hit_str_count += hit_str_num
try:
recall = global_accumulative_recall / total_num_gt
except ZeroDivisionError:
......
tools/infer/predict_e2e.py
浏览文件 @ a0d1f923
......@@ -151,7 +151,7 @@ if __name__ == "__main__":
src_im = utility.draw_e2e_res(points, strs, image_file)
img_name_pure = os.path.split(image_file)[-1]
img_path = os.path.join(draw_img_save,
"e2e_res_{}_pgnet".format(img_name_pure))
"e2e_res_{}".format(img_name_pure))
cv2.imwrite(img_path, src_im)
logger.info("The visualized image saved in {}".format(img_path))
if count > 1:
......
train_data/total_text/train/poly/2.txt 已删除 100644 → 0
浏览文件 @ 0cd48c35
2.0,165.0,20.0,167.0,39.0,170.0,57.0,173.0,76.0,176.0,94.0,179.0,113.0,182.0,109.0,218.0,90.0,215.0,72.0,213.0,54.0,210.0,36.0,208.0,18.0,205.0,0.0,203.0 izza
2.0,411.0,30.0,412.0,58.0,414.0,87.0,416.0,115.0,418.0,143.0,420.0,172.0,422.0,172.0,476.0,143.0,474.0,114.0,472.0,86.0,471.0,57.0,469.0,28.0,467.0,0.0,466.0 ISA
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或