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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is refered from:
https://github.com/shengtao96/CentripetalText/blob/main/test.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path as osp
import numpy as np
import cv2
import paddle
import pyclipper
class CTPostProcess(object):
"""
The post process for Centripetal Text (CT).
"""
def __init__(self, min_score=0.88, min_area=16, box_type='poly', **kwargs):
self.min_score = min_score
self.min_area = min_area
self.box_type = box_type
self.coord = np.zeros((2, 300, 300), dtype=np.int32)
for i in range(300):
for j in range(300):
self.coord[0, i, j] = j
self.coord[1, i, j] = i
def __call__(self, preds, batch):
outs = preds['maps']
out_scores = preds['score']
if isinstance(outs, paddle.Tensor):
outs = outs.numpy()
if isinstance(out_scores, paddle.Tensor):
out_scores = out_scores.numpy()
batch_size = outs.shape[0]
boxes_batch = []
for idx in range(batch_size):
bboxes = []
scores = []
img_shape = batch[idx]
org_img_size = img_shape[:3]
img_shape = img_shape[3:]
img_size = img_shape[:2]
out = np.expand_dims(outs[idx], axis=0)
outputs = dict()
score = np.expand_dims(out_scores[idx], axis=0)
kernel = out[:, 0, :, :] > 0.2
loc = out[:, 1:, :, :].astype("float32")
score = score[0].astype(np.float32)
kernel = kernel[0].astype(np.uint8)
loc = loc[0].astype(np.float32)
label_num, label_kernel = cv2.connectedComponents(
kernel, connectivity=4)
for i in range(1, label_num):
ind = (label_kernel == i)
if ind.sum(
) < 10: # pixel number less than 10, treated as background
label_kernel[ind] = 0
label = np.zeros_like(label_kernel)
h, w = label_kernel.shape
pixels = self.coord[:, :h, :w].reshape(2, -1)
points = pixels.transpose([1, 0]).astype(np.float32)
off_points = (points + 10. / 4. * loc[:, pixels[1], pixels[0]].T
).astype(np.int32)
off_points[:, 0] = np.clip(off_points[:, 0], 0, label.shape[1] - 1)
off_points[:, 1] = np.clip(off_points[:, 1], 0, label.shape[0] - 1)
label[pixels[1], pixels[0]] = label_kernel[off_points[:, 1],
off_points[:, 0]]
label[label_kernel > 0] = label_kernel[label_kernel > 0]
score_pocket = [0.0]
for i in range(1, label_num):
ind = (label_kernel == i)
if ind.sum() == 0:
score_pocket.append(0.0)
continue
score_i = np.mean(score[ind])
score_pocket.append(score_i)
label_num = np.max(label) + 1
label = cv2.resize(
label, (img_size[1], img_size[0]),
interpolation=cv2.INTER_NEAREST)
scale = (float(org_img_size[1]) / float(img_size[1]),
float(org_img_size[0]) / float(img_size[0]))
for i in range(1, label_num):
ind = (label == i)
points = np.array(np.where(ind)).transpose((1, 0))
if points.shape[0] < self.min_area:
continue
score_i = score_pocket[i]
if score_i < self.min_score:
continue
if self.box_type == 'rect':
rect = cv2.minAreaRect(points[:, ::-1])
bbox = cv2.boxPoints(rect) * scale
z = bbox.mean(0)
bbox = z + (bbox - z) * 0.85
elif self.box_type == 'poly':
binary = np.zeros(label.shape, dtype='uint8')
binary[ind] = 1
try:
_, contours, _ = cv2.findContours(
binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
except BaseException:
contours, _ = cv2.findContours(
binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
bbox = contours[0] * scale
bbox = bbox.astype('int32')
bboxes.append(bbox.reshape(-1, 2))
scores.append(score_i)
boxes_batch.append({'points': bboxes})
return boxes_batch
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