# 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/WenmuZhou/DBNet.pytorch/blob/master/post_processing/seg_detector_representer.py """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import cv2 import paddle from shapely.geometry import Polygon import pyclipper class DBPostProcess(object): """ The post process for Differentiable Binarization (DB). """ def __init__(self, thresh=0.3, box_thresh=0.7, max_candidates=1000, unclip_ratio=2.0, use_dilation=False, score_mode="fast", box_type='quad', **kwargs): self.thresh = thresh self.box_thresh = box_thresh self.max_candidates = max_candidates self.unclip_ratio = unclip_ratio self.min_size = 3 self.score_mode = score_mode self.box_type = box_type assert score_mode in [ "slow", "fast" ], "Score mode must be in [slow, fast] but got: {}".format(score_mode) self.dilation_kernel = None if not use_dilation else np.array( [[1, 1], [1, 1]]) def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height): ''' _bitmap: single map with shape (1, H, W), whose values are binarized as {0, 1} ''' bitmap = _bitmap height, width = bitmap.shape boxes = [] scores = [] contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) for contour in contours[:self.max_candidates]: epsilon = 0.002 * cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, epsilon, True) points = approx.reshape((-1, 2)) if points.shape[0] < 4: continue score = self.box_score_fast(pred, points.reshape(-1, 2)) if self.box_thresh > score: continue if points.shape[0] > 2: box = self.unclip(points, self.unclip_ratio) if len(box) > 1: continue else: continue box = box.reshape(-1, 2) _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2))) if sside < self.min_size + 2: continue box = np.array(box) box[:, 0] = np.clip( np.round(box[:, 0] / width * dest_width), 0, dest_width) box[:, 1] = np.clip( np.round(box[:, 1] / height * dest_height), 0, dest_height) boxes.append(box.tolist()) scores.append(score) return boxes, scores def boxes_from_bitmap(self, pred, _bitmap,classes, dest_width, dest_height): ''' _bitmap: single map with shape (1, H, W), whose values are binarized as {0, 1} ''' bitmap = _bitmap height, width = bitmap.shape outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) if len(outs) == 3: img, contours, _ = outs[0], outs[1], outs[2] elif len(outs) == 2: contours, _ = outs[0], outs[1] num_contours = min(len(contours), self.max_candidates) boxes = [] scores = [] class_indexes = [] class_scores = [] for index in range(num_contours): contour = contours[index] points, sside = self.get_mini_boxes(contour) if sside < self.min_size: continue points = np.array(points) if self.score_mode == "fast": score, class_index, class_score = self.box_score_fast(pred, points.reshape(-1, 2), classes) else: score, class_index, class_score = self.box_score_slow(pred, contour, classes) print("origin score:" + str(score)) if self.box_thresh > score: continue box = self.unclip(points, self.unclip_ratio).reshape(-1, 1, 2) box, sside = self.get_mini_boxes(box) if sside < self.min_size + 2: continue box = np.array(box) box[:, 0] = np.clip( np.round(box[:, 0] / width * dest_width), 0, dest_width) box[:, 1] = np.clip( np.round(box[:, 1] / height * dest_height), 0, dest_height) boxes.append(box.astype("int32")) scores.append(score) class_indexes.append(class_index) class_scores.append(class_score) if classes is None: return np.array(boxes, dtype="int32"), scores else: return np.array(boxes, dtype="int32"), scores, class_indexes, class_scores def unclip(self, box, unclip_ratio): poly = Polygon(box) distance = poly.area * unclip_ratio / poly.length offset = pyclipper.PyclipperOffset() offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) expanded = np.array(offset.Execute(distance)) return expanded def get_mini_boxes(self, contour): bounding_box = cv2.minAreaRect(contour) points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0]) index_1, index_2, index_3, index_4 = 0, 1, 2, 3 if points[1][1] > points[0][1]: index_1 = 0 index_4 = 1 else: index_1 = 1 index_4 = 0 if points[3][1] > points[2][1]: index_2 = 2 index_3 = 3 else: index_2 = 3 index_3 = 2 box = [ points[index_1], points[index_2], points[index_3], points[index_4] ] return box, min(bounding_box[1]) def box_score_fast(self, bitmap, _box,classes): ''' box_score_fast: use bbox mean score as the mean score ''' # print(classes) h, w = bitmap.shape[:2] box = _box.copy() xmin = np.clip(np.floor(box[:, 0].min()).astype("int32"), 0, w - 1) xmax = np.clip(np.ceil(box[:, 0].max()).astype("int32"), 0, w - 1) ymin = np.clip(np.floor(box[:, 1].min()).astype("int32"), 0, h - 1) ymax = np.clip(np.ceil(box[:, 1].max()).astype("int32"), 0, h - 1) # box__ = box.reshape(1, -1, 2) mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) box[:, 0] = box[:, 0] - xmin box[:, 1] = box[:, 1] - ymin cv2.fillPoly(mask, box.reshape(1, -1, 2).astype("int32"), 1) if classes is None: return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0], None, None else: k = 255 class_mask = np.full((ymax - ymin + 1, xmax - xmin + 1), k, dtype=np.int32) cv2.fillPoly(class_mask, box.reshape(1, -1, 2).astype(np.int32), 0) classes = classes[ymin:ymax + 1, xmin:xmax + 1] new_classes = classes + class_mask # 拉平 a = new_classes.reshape(-1) b = np.where(a >= k) # print(len(b[0].tolist())) classes = np.delete(a, b[0].tolist()) class_index = np.argmax(np.bincount(classes)) print(class_index) class_score = np.sum(classes == class_index) / len(classes) print(class_score) return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0], class_index, class_score def box_score_slow(self, bitmap, contour,classes): ''' box_score_slow: use polyon mean score as the mean score ''' h, w = bitmap.shape[:2] contour = contour.copy() contour = np.reshape(contour, (-1, 2)) xmin = np.clip(np.min(contour[:, 0]), 0, w - 1) xmax = np.clip(np.max(contour[:, 0]), 0, w - 1) ymin = np.clip(np.min(contour[:, 1]), 0, h - 1) ymax = np.clip(np.max(contour[:, 1]), 0, h - 1) mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) contour[:, 0] = contour[:, 0] - xmin contour[:, 1] = contour[:, 1] - ymin cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype("int32"), 1) if classes is None: return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0], None, None else: k = 999 class_mask = np.full((ymax - ymin + 1, xmax - xmin + 1), k, dtype=np.int32) cv2.fillPoly(class_mask, contour.reshape(1, -1, 2).astype("int32"), 0) classes = classes[ymin:ymax + 1, xmin:xmax + 1] new_classes = classes + class_mask # 拉平 a = new_classes.reshape(-1) b = np.where(a >= k) classes = np.delete(a, b[0].tolist()) class_index = np.argmax(np.bincount(classes)) class_score = np.sum(classes == class_index) / len(classes) return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0], class_index, class_score def __call__(self, outs_dict, shape_list): pred = outs_dict['maps'] if isinstance(pred, paddle.Tensor): pred = pred.numpy() pred = pred[:, 0, :, :] segmentation = pred > self.thresh print(pred.shape) if "classes" in outs_dict: classes = outs_dict['classes'] # print(classes) # print("jerome1") # print(classes.shape) # print(classes) # np.set_printoptions(threshold=np.inf) if isinstance(classes, paddle.Tensor): # classes = paddle.argmax(classes, axis=1, dtype='int32') classes = classes.numpy() # else: # classes = np.argmax(classes, axis=1).astype(np.int32) classes = classes[:, 0, :, :] print(classes.shape) else: classes = None boxes_batch = [] for batch_index in range(pred.shape[0]): src_h, src_w, ratio_h, ratio_w = shape_list[batch_index] if self.dilation_kernel is not None: mask = cv2.dilate( np.array(segmentation[batch_index]).astype(np.uint8), self.dilation_kernel) else: mask = segmentation[batch_index] if self.box_type == 'poly': boxes, scores = self.polygons_from_bitmap(pred[batch_index], mask, src_w, src_h) elif self.box_type == 'quad': if classes is None: boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, None, src_w, src_h) else: boxes, scores, class_indexes, class_scores = self.boxes_from_bitmap(pred[batch_index], mask, classes[batch_index], src_w, src_h) boxes_batch.append({'points': boxes, "classes": class_indexes, "class_scores": class_scores}) else: raise ValueError("box_type can only be one of ['quad', 'poly']") boxes_batch.append({'points': boxes}) return boxes_batch class DistillationDBPostProcess(object): def __init__(self, model_name=["student"], key=None, thresh=0.3, box_thresh=0.6, max_candidates=1000, unclip_ratio=1.5, use_dilation=False, score_mode="fast", box_type='quad', **kwargs): self.model_name = model_name self.key = key self.post_process = DBPostProcess( thresh=thresh, box_thresh=box_thresh, max_candidates=max_candidates, unclip_ratio=unclip_ratio, use_dilation=use_dilation, score_mode=score_mode, box_type=box_type) def __call__(self, predicts, shape_list): results = {} for k in self.model_name: results[k] = self.post_process(predicts[k], shape_list=shape_list) return results