# copyright (c) 2022 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,
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"""
This code is refer from:
https://github.com/open-mmlab/mmocr/blob/v0.3.0/mmocr/models/textdet/postprocess/wrapper.py
"""
import cv2
import paddle
import numpy as np
from numpy.fft import ifft
from ppocr.utils.poly_nms import poly_nms, valid_boundary
def fill_hole(input_mask):
h, w = input_mask.shape
canvas = np.zeros((h + 2, w + 2), np.uint8)
canvas[1:h + 1, 1:w + 1] = input_mask.copy()
mask = np.zeros((h + 4, w + 4), np.uint8)
cv2.floodFill(canvas, mask, (0, 0), 1)
canvas = canvas[1:h + 1, 1:w + 1].astype(np.bool_)
return ~canvas | input_mask
def fourier2poly(fourier_coeff, num_reconstr_points=50):
""" Inverse Fourier transform
Args:
fourier_coeff (ndarray): Fourier coefficients shaped (n, 2k+1),
with n and k being candidates number and Fourier degree
respectively.
num_reconstr_points (int): Number of reconstructed polygon points.
Returns:
Polygons (ndarray): The reconstructed polygons shaped (n, n')
"""
a = np.zeros((len(fourier_coeff), num_reconstr_points), dtype='complex')
k = (len(fourier_coeff[0]) - 1) // 2
a[:, 0:k + 1] = fourier_coeff[:, k:]
a[:, -k:] = fourier_coeff[:, :k]
poly_complex = ifft(a) * num_reconstr_points
polygon = np.zeros((len(fourier_coeff), num_reconstr_points, 2))
polygon[:, :, 0] = poly_complex.real
polygon[:, :, 1] = poly_complex.imag
return polygon.astype('int32').reshape((len(fourier_coeff), -1))
class FCEPostProcess(object):
"""
The post process for FCENet.
"""
def __init__(self,
scales,
fourier_degree=5,
num_reconstr_points=50,
decoding_type='fcenet',
score_thr=0.3,
nms_thr=0.1,
alpha=1.0,
beta=1.0,
box_type='poly',
**kwargs):
self.scales = scales
self.fourier_degree = fourier_degree
self.num_reconstr_points = num_reconstr_points
self.decoding_type = decoding_type
self.score_thr = score_thr
self.nms_thr = nms_thr
self.alpha = alpha
self.beta = beta
self.box_type = box_type
def __call__(self, preds, shape_list):
score_maps = []
for key, value in preds.items():
if isinstance(value, paddle.Tensor):
value = value.numpy()
cls_res = value[:, :4, :, :]
reg_res = value[:, 4:, :, :]
score_maps.append([cls_res, reg_res])
return self.get_boundary(score_maps, shape_list)
def resize_boundary(self, boundaries, scale_factor):
"""Rescale boundaries via scale_factor.
Args:
boundaries (list[list[float]]): The boundary list. Each boundary
with size 2k+1 with k>=4.
scale_factor(ndarray): The scale factor of size (4,).
Returns:
boundaries (list[list[float]]): The scaled boundaries.
"""
boxes = []
scores = []
for b in boundaries:
sz = len(b)
valid_boundary(b, True)
scores.append(b[-1])
b = (np.array(b[:sz - 1]) *
(np.tile(scale_factor[:2], int(
(sz - 1) / 2)).reshape(1, sz - 1))).flatten().tolist()
boxes.append(np.array(b).reshape([-1, 2]))
return np.array(boxes, dtype=np.float32), scores
def get_boundary(self, score_maps, shape_list):
assert len(score_maps) == len(self.scales)
boundaries = []
for idx, score_map in enumerate(score_maps):
scale = self.scales[idx]
boundaries = boundaries + self._get_boundary_single(score_map,
scale)
# nms
boundaries = poly_nms(boundaries, self.nms_thr)
boundaries, scores = self.resize_boundary(
boundaries, (1 / shape_list[0, 2:]).tolist()[::-1])
boxes_batch = [dict(points=boundaries, scores=scores)]
return boxes_batch
def _get_boundary_single(self, score_map, scale):
assert len(score_map) == 2
assert score_map[1].shape[1] == 4 * self.fourier_degree + 2
return self.fcenet_decode(
preds=score_map,
fourier_degree=self.fourier_degree,
num_reconstr_points=self.num_reconstr_points,
scale=scale,
alpha=self.alpha,
beta=self.beta,
box_type=self.box_type,
score_thr=self.score_thr,
nms_thr=self.nms_thr)
def fcenet_decode(self,
preds,
fourier_degree,
num_reconstr_points,
scale,
alpha=1.0,
beta=2.0,
box_type='poly',
score_thr=0.3,
nms_thr=0.1):
"""Decoding predictions of FCENet to instances.
Args:
preds (list(Tensor)): The head output tensors.
fourier_degree (int): The maximum Fourier transform degree k.
num_reconstr_points (int): The points number of the polygon
reconstructed from predicted Fourier coefficients.
scale (int): The down-sample scale of the prediction.
alpha (float) : The parameter to calculate final scores. Score_{final}
= (Score_{text region} ^ alpha)
* (Score_{text center region}^ beta)
beta (float) : The parameter to calculate final score.
box_type (str): Boundary encoding type 'poly' or 'quad'.
score_thr (float) : The threshold used to filter out the final
candidates.
nms_thr (float) : The threshold of nms.
Returns:
boundaries (list[list[float]]): The instance boundary and confidence
list.
"""
assert isinstance(preds, list)
assert len(preds) == 2
assert box_type in ['poly', 'quad']
cls_pred = preds[0][0]
tr_pred = cls_pred[0:2]
tcl_pred = cls_pred[2:]
reg_pred = preds[1][0].transpose([1, 2, 0])
x_pred = reg_pred[:, :, :2 * fourier_degree + 1]
y_pred = reg_pred[:, :, 2 * fourier_degree + 1:]
score_pred = (tr_pred[1]**alpha) * (tcl_pred[1]**beta)
tr_pred_mask = (score_pred) > score_thr
tr_mask = fill_hole(tr_pred_mask)
tr_contours, _ = cv2.findContours(
tr_mask.astype(np.uint8), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE) # opencv4
mask = np.zeros_like(tr_mask)
boundaries = []
for cont in tr_contours:
deal_map = mask.copy().astype(np.int8)
cv2.drawContours(deal_map, [cont], -1, 1, -1)
score_map = score_pred * deal_map
score_mask = score_map > 0
xy_text = np.argwhere(score_mask)
dxy = xy_text[:, 1] + xy_text[:, 0] * 1j
x, y = x_pred[score_mask], y_pred[score_mask]
c = x + y * 1j
c[:, fourier_degree] = c[:, fourier_degree] + dxy
c *= scale
polygons = fourier2poly(c, num_reconstr_points)
score = score_map[score_mask].reshape(-1, 1)
polygons = poly_nms(np.hstack((polygons, score)).tolist(), nms_thr)
boundaries = boundaries + polygons
boundaries = poly_nms(boundaries, nms_thr)
if box_type == 'quad':
new_boundaries = []
for boundary in boundaries:
poly = np.array(boundary[:-1]).reshape(-1, 2).astype(np.float32)
score = boundary[-1]
points = cv2.boxPoints(cv2.minAreaRect(poly))
points = np.int64(points)
new_boundaries.append(points.reshape(-1).tolist() + [score])
boundaries = new_boundaries
return boundaries