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# 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.
"""
This code is refer from:
https://github.com/whai362/PSENet/blob/python3/models/head/psenet_head.py
"""
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='quad',
scale=4,
**kwargs):
assert box_type in ['quad', 'poly'], 'Only quad 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, :, :]
text_mask = paddle.unsqueeze(text_mask, axis=1)
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 == 'quad':
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
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