277 lines
9.7 KiB
Python
277 lines
9.7 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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"""
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This code is refered from:
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https://github.com/WenmuZhou/DBNet.pytorch/blob/master/post_processing/seg_detector_representer.py
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"""
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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 numpy as np
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import cv2
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# import paddle
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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,
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thresh=0.3,
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box_thresh=0.7,
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max_candidates=1000,
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unclip_ratio=2.0,
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use_dilation=False,
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score_mode="fast",
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box_type='quad',
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**kwargs):
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self.thresh = thresh
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self.box_thresh = box_thresh
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self.max_candidates = max_candidates
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self.unclip_ratio = unclip_ratio
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self.min_size = 3
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self.score_mode = score_mode
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self.box_type = box_type
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assert score_mode in [
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"slow", "fast"
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], "Score mode must be in [slow, fast] but got: {}".format(score_mode)
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self.dilation_kernel = None if not use_dilation else np.array(
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[[1, 1], [1, 1]])
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def polygons_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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boxes = []
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scores = []
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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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for contour in contours[:self.max_candidates]:
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epsilon = 0.002 * cv2.arcLength(contour, True)
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approx = cv2.approxPolyDP(contour, epsilon, True)
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points = approx.reshape((-1, 2))
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if points.shape[0] < 4:
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continue
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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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if points.shape[0] > 2:
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box = self.unclip(points, self.unclip_ratio)
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if len(box) > 1:
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continue
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else:
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continue
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box = box.reshape(-1, 2)
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_, sside = self.get_mini_boxes(box.reshape((-1, 1, 2)))
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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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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.append(box.tolist())
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scores.append(score)
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return boxes, scores
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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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outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST,
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cv2.CHAIN_APPROX_SIMPLE)
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if len(outs) == 3:
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img, contours, _ = outs[0], outs[1], outs[2]
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elif len(outs) == 2:
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contours, _ = outs[0], outs[1]
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num_contours = min(len(contours), self.max_candidates)
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boxes = []
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scores = []
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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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if self.score_mode == "fast":
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score = self.box_score_fast(pred, points.reshape(-1, 2))
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else:
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score = self.box_score_slow(pred, contour)
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if self.box_thresh > score:
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continue
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box = self.unclip(points, self.unclip_ratio).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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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.append(box.astype("int32"))
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scores.append(score)
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return np.array(boxes, dtype="int32"), scores
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def unclip(self, box, unclip_ratio):
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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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'''
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box_score_fast: use bbox mean score as the mean score
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'''
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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("int32"), 0, w - 1)
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xmax = np.clip(np.ceil(box[:, 0].max()).astype("int32"), 0, w - 1)
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ymin = np.clip(np.floor(box[:, 1].min()).astype("int32"), 0, h - 1)
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ymax = np.clip(np.ceil(box[:, 1].max()).astype("int32"), 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("int32"), 1)
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return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
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def box_score_slow(self, bitmap, contour):
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'''
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box_score_slow: use polyon mean score as the mean score
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'''
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h, w = bitmap.shape[:2]
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contour = contour.copy()
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contour = np.reshape(contour, (-1, 2))
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xmin = np.clip(np.min(contour[:, 0]), 0, w - 1)
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xmax = np.clip(np.max(contour[:, 0]), 0, w - 1)
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ymin = np.clip(np.min(contour[:, 1]), 0, h - 1)
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ymax = np.clip(np.max(contour[:, 1]), 0, h - 1)
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mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
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contour[:, 0] = contour[:, 0] - xmin
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contour[:, 1] = contour[:, 1] - ymin
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cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype("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, shape_list):
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pred = outs_dict['maps']
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# if isinstance(pred, paddle.Tensor):
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# pred = pred.numpy()
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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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src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
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if self.dilation_kernel is not None:
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mask = cv2.dilate(
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np.array(segmentation[batch_index]).astype(np.uint8),
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self.dilation_kernel)
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else:
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mask = segmentation[batch_index]
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if self.box_type == 'poly':
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boxes, scores = self.polygons_from_bitmap(pred[batch_index],
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mask, src_w, src_h)
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elif self.box_type == 'quad':
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boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
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src_w, src_h)
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else:
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raise ValueError("box_type can only be one of ['quad', 'poly']")
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boxes_batch.append({'points': boxes})
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return boxes_batch
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class DistillationDBPostProcess(object):
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def __init__(self,
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model_name=["student"],
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key=None,
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thresh=0.3,
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box_thresh=0.6,
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max_candidates=1000,
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unclip_ratio=1.5,
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use_dilation=False,
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score_mode="fast",
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box_type='quad',
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**kwargs):
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self.model_name = model_name
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self.key = key
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self.post_process = DBPostProcess(
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thresh=thresh,
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box_thresh=box_thresh,
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max_candidates=max_candidates,
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unclip_ratio=unclip_ratio,
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use_dilation=use_dilation,
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score_mode=score_mode,
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box_type=box_type)
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def __call__(self, predicts, shape_list):
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results = {}
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for k in self.model_name:
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results[k] = self.post_process(predicts[k], shape_list=shape_list)
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return results
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