242 lines
8.4 KiB
Python
242 lines
8.4 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is refer from:
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https://github.com/open-mmlab/mmocr/blob/v0.3.0/mmocr/models/textdet/postprocess/wrapper.py
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"""
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import cv2
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import paddle
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import numpy as np
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from numpy.fft import ifft
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from ppocr.utils.poly_nms import poly_nms, valid_boundary
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def fill_hole(input_mask):
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h, w = input_mask.shape
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canvas = np.zeros((h + 2, w + 2), np.uint8)
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canvas[1:h + 1, 1:w + 1] = input_mask.copy()
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mask = np.zeros((h + 4, w + 4), np.uint8)
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cv2.floodFill(canvas, mask, (0, 0), 1)
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canvas = canvas[1:h + 1, 1:w + 1].astype(np.bool)
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return ~canvas | input_mask
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def fourier2poly(fourier_coeff, num_reconstr_points=50):
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""" Inverse Fourier transform
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Args:
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fourier_coeff (ndarray): Fourier coefficients shaped (n, 2k+1),
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with n and k being candidates number and Fourier degree
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respectively.
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num_reconstr_points (int): Number of reconstructed polygon points.
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Returns:
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Polygons (ndarray): The reconstructed polygons shaped (n, n')
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"""
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a = np.zeros((len(fourier_coeff), num_reconstr_points), dtype='complex')
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k = (len(fourier_coeff[0]) - 1) // 2
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a[:, 0:k + 1] = fourier_coeff[:, k:]
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a[:, -k:] = fourier_coeff[:, :k]
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poly_complex = ifft(a) * num_reconstr_points
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polygon = np.zeros((len(fourier_coeff), num_reconstr_points, 2))
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polygon[:, :, 0] = poly_complex.real
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polygon[:, :, 1] = poly_complex.imag
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return polygon.astype('int32').reshape((len(fourier_coeff), -1))
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class FCEPostProcess(object):
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"""
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The post process for FCENet.
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"""
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def __init__(self,
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scales,
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fourier_degree=5,
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num_reconstr_points=50,
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decoding_type='fcenet',
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score_thr=0.3,
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nms_thr=0.1,
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alpha=1.0,
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beta=1.0,
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box_type='poly',
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**kwargs):
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self.scales = scales
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self.fourier_degree = fourier_degree
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self.num_reconstr_points = num_reconstr_points
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self.decoding_type = decoding_type
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self.score_thr = score_thr
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self.nms_thr = nms_thr
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self.alpha = alpha
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self.beta = beta
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self.box_type = box_type
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def __call__(self, preds, shape_list):
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score_maps = []
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for key, value in preds.items():
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if isinstance(value, paddle.Tensor):
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value = value.numpy()
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cls_res = value[:, :4, :, :]
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reg_res = value[:, 4:, :, :]
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score_maps.append([cls_res, reg_res])
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return self.get_boundary(score_maps, shape_list)
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def resize_boundary(self, boundaries, scale_factor):
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"""Rescale boundaries via scale_factor.
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Args:
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boundaries (list[list[float]]): The boundary list. Each boundary
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with size 2k+1 with k>=4.
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scale_factor(ndarray): The scale factor of size (4,).
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Returns:
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boundaries (list[list[float]]): The scaled boundaries.
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"""
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boxes = []
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scores = []
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for b in boundaries:
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sz = len(b)
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valid_boundary(b, True)
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scores.append(b[-1])
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b = (np.array(b[:sz - 1]) *
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(np.tile(scale_factor[:2], int(
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(sz - 1) / 2)).reshape(1, sz - 1))).flatten().tolist()
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boxes.append(np.array(b).reshape([-1, 2]))
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return np.array(boxes, dtype=np.float32), scores
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def get_boundary(self, score_maps, shape_list):
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assert len(score_maps) == len(self.scales)
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boundaries = []
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for idx, score_map in enumerate(score_maps):
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scale = self.scales[idx]
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boundaries = boundaries + self._get_boundary_single(score_map,
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scale)
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# nms
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boundaries = poly_nms(boundaries, self.nms_thr)
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boundaries, scores = self.resize_boundary(
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boundaries, (1 / shape_list[0, 2:]).tolist()[::-1])
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boxes_batch = [dict(points=boundaries, scores=scores)]
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return boxes_batch
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def _get_boundary_single(self, score_map, scale):
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assert len(score_map) == 2
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assert score_map[1].shape[1] == 4 * self.fourier_degree + 2
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return self.fcenet_decode(
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preds=score_map,
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fourier_degree=self.fourier_degree,
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num_reconstr_points=self.num_reconstr_points,
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scale=scale,
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alpha=self.alpha,
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beta=self.beta,
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box_type=self.box_type,
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score_thr=self.score_thr,
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nms_thr=self.nms_thr)
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def fcenet_decode(self,
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preds,
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fourier_degree,
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num_reconstr_points,
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scale,
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alpha=1.0,
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beta=2.0,
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box_type='poly',
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score_thr=0.3,
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nms_thr=0.1):
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"""Decoding predictions of FCENet to instances.
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Args:
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preds (list(Tensor)): The head output tensors.
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fourier_degree (int): The maximum Fourier transform degree k.
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num_reconstr_points (int): The points number of the polygon
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reconstructed from predicted Fourier coefficients.
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scale (int): The down-sample scale of the prediction.
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alpha (float) : The parameter to calculate final scores. Score_{final}
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= (Score_{text region} ^ alpha)
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* (Score_{text center region}^ beta)
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beta (float) : The parameter to calculate final score.
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box_type (str): Boundary encoding type 'poly' or 'quad'.
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score_thr (float) : The threshold used to filter out the final
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candidates.
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nms_thr (float) : The threshold of nms.
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Returns:
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boundaries (list[list[float]]): The instance boundary and confidence
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list.
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"""
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assert isinstance(preds, list)
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assert len(preds) == 2
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assert box_type in ['poly', 'quad']
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cls_pred = preds[0][0]
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tr_pred = cls_pred[0:2]
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tcl_pred = cls_pred[2:]
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reg_pred = preds[1][0].transpose([1, 2, 0])
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x_pred = reg_pred[:, :, :2 * fourier_degree + 1]
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y_pred = reg_pred[:, :, 2 * fourier_degree + 1:]
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score_pred = (tr_pred[1]**alpha) * (tcl_pred[1]**beta)
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tr_pred_mask = (score_pred) > score_thr
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tr_mask = fill_hole(tr_pred_mask)
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tr_contours, _ = cv2.findContours(
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tr_mask.astype(np.uint8), cv2.RETR_TREE,
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cv2.CHAIN_APPROX_SIMPLE) # opencv4
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mask = np.zeros_like(tr_mask)
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boundaries = []
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for cont in tr_contours:
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deal_map = mask.copy().astype(np.int8)
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cv2.drawContours(deal_map, [cont], -1, 1, -1)
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score_map = score_pred * deal_map
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score_mask = score_map > 0
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xy_text = np.argwhere(score_mask)
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dxy = xy_text[:, 1] + xy_text[:, 0] * 1j
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x, y = x_pred[score_mask], y_pred[score_mask]
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c = x + y * 1j
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c[:, fourier_degree] = c[:, fourier_degree] + dxy
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c *= scale
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polygons = fourier2poly(c, num_reconstr_points)
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score = score_map[score_mask].reshape(-1, 1)
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polygons = poly_nms(np.hstack((polygons, score)).tolist(), nms_thr)
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boundaries = boundaries + polygons
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boundaries = poly_nms(boundaries, nms_thr)
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if box_type == 'quad':
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new_boundaries = []
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for boundary in boundaries:
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poly = np.array(boundary[:-1]).reshape(-1, 2).astype(np.float32)
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score = boundary[-1]
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points = cv2.boxPoints(cv2.minAreaRect(poly))
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points = np.int0(points)
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new_boundaries.append(points.reshape(-1).tolist() + [score])
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boundaries = new_boundaries
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return boundaries
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