154 lines
5.4 KiB
Python
154 lines
5.4 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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import paddle.fluid as fluid
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import numpy as np
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import string
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import cv2
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from shapely.geometry import Polygon
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import pyclipper
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class DBPostProcess(object):
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"""
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The post process for Differentiable Binarization (DB).
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"""
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def __init__(self, params):
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self.thresh = params['thresh']
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self.box_thresh = params['box_thresh']
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self.max_candidates = params['max_candidates']
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self.min_size = 3
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def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
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'''
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_bitmap: single map with shape (1, H, W),
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whose values are binarized as {0, 1}
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'''
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bitmap = _bitmap
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height, width = bitmap.shape
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# img, contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
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contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8),
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cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
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num_contours = min(len(contours), self.max_candidates)
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boxes = np.zeros((num_contours, 4, 2), dtype=np.int16)
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scores = np.zeros((num_contours, ), dtype=np.float32)
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for index in range(num_contours):
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contour = contours[index]
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points, sside = self.get_mini_boxes(contour)
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if sside < self.min_size:
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continue
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points = np.array(points)
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score = self.box_score_fast(pred, points.reshape(-1, 2))
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if self.box_thresh > score:
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continue
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box = self.unclip(points).reshape(-1, 1, 2)
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box, sside = self.get_mini_boxes(box)
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if sside < self.min_size + 2:
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continue
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box = np.array(box)
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if not isinstance(dest_width, int):
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dest_width = dest_width.item()
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dest_height = dest_height.item()
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box[:, 0] = np.clip(
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np.round(box[:, 0] / width * dest_width), 0, dest_width)
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box[:, 1] = np.clip(
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np.round(box[:, 1] / height * dest_height), 0, dest_height)
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boxes[index, :, :] = box.astype(np.int16)
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scores[index] = score
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return boxes, scores
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def unclip(self, box, unclip_ratio=2.0):
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poly = Polygon(box)
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distance = poly.area * unclip_ratio / poly.length
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offset = pyclipper.PyclipperOffset()
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offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
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expanded = np.array(offset.Execute(distance))
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return expanded
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def get_mini_boxes(self, contour):
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bounding_box = cv2.minAreaRect(contour)
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points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
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index_1, index_2, index_3, index_4 = 0, 1, 2, 3
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if points[1][1] > points[0][1]:
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index_1 = 0
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index_4 = 1
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else:
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index_1 = 1
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index_4 = 0
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if points[3][1] > points[2][1]:
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index_2 = 2
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index_3 = 3
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else:
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index_2 = 3
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index_3 = 2
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box = [
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points[index_1], points[index_2], points[index_3], points[index_4]
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]
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return box, min(bounding_box[1])
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def box_score_fast(self, bitmap, _box):
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h, w = bitmap.shape[:2]
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box = _box.copy()
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xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
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xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
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ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
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ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
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mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
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box[:, 0] = box[:, 0] - xmin
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box[:, 1] = box[:, 1] - ymin
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cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
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return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
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def __call__(self, outs_dict, ratio_list):
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pred = outs_dict['maps']
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pred = pred[:, 0, :, :]
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segmentation = pred > self.thresh
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boxes_batch = []
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for batch_index in range(pred.shape[0]):
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height, width = pred.shape[-2:]
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tmp_boxes, tmp_scores = self.boxes_from_bitmap(
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pred[batch_index], segmentation[batch_index], width, height)
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boxes = []
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for k in range(len(tmp_boxes)):
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if tmp_scores[k] > self.box_thresh:
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boxes.append(tmp_boxes[k])
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if len(boxes) > 0:
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boxes = np.array(boxes)
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ratio_h, ratio_w = ratio_list[batch_index]
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boxes[:, :, 0] = boxes[:, :, 0] / ratio_w
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boxes[:, :, 1] = boxes[:, :, 1] / ratio_h
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boxes_batch.append(boxes)
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return boxes_batch
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