import cv2 import numpy as np import pyclipper from shapely.geometry import Polygon from pyclipper import PyclipperOffset import math import operator from functools import reduce def clockwise_sort_points(_point_coordinates): """ 以左上角为起点的顺时针排序 原理就是将笛卡尔坐标转换为极坐标，然后对极坐标的φ进行排序 Args: _point_coordinates: 待排序的点[(x,y),] Returns: 排序完成的点 """ center_point = tuple( map(operator.truediv, reduce(lambda x, y: map(operator.add, x, y), _point_coordinates), [len(_point_coordinates)] * 2)) return sorted(_point_coordinates, key=lambda coord: (180 + math.degrees( math.atan2(*tuple(map(operator.sub, coord, center_point))[::-1]))) % 360) class DistillationDBPostProcess(object): def __init__(self, model_name=None, key=None, thresh=0.3, box_thresh=0.6, max_candidates=1000, unclip_ratio=1.5, use_dilation=False, score_mode="fast", **kwargs): if model_name is None: model_name = ["student"] 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) def __call__(self, predicts, shape_list): results = {} for k in self.model_name: results[k] = self.post_process(predicts[k].detach().cpu().numpy(), shape_list=shape_list) return results class DBPostProcess(object): """ The post process for Differentiable Binarization (DB). """ def __init__(self, thresh=0.6, box_thresh=0.6, max_candidates=1000, unclip_ratio=1.5, use_dilation=False, **kwargs): self.thresh = thresh self.box_thresh = box_thresh self.max_candidates = max_candidates self.unclip_ratio = unclip_ratio self.min_size = 3 self.dilation_kernel = None if not use_dilation else np.array( [[1, 1], [1, 1]]) def boxes_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 bitmap = (bitmap * 255).astype(np.uint8) # structure_element = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) # bitmap = cv2.morphologyEx(bitmap, cv2.MORPH_CLOSE, structure_element) # cv2.imwrite('bin.jpg',bitmap) if cv2.__version__.startswith('3'): _, contours, _ = cv2.findContours(bitmap, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) elif cv2.__version__.startswith('4'): contours, _ = cv2.findContours(bitmap, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) else: raise NotImplementedError(f'opencv {cv2.__version__} not support') num_contours = min(len(contours), self.max_candidates) boxes = [] 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) score = self.box_score_fast(pred, points.reshape(-1, 2)) # score = self.box_score_slow(pred, contour) if score < self.box_thresh: continue box = self.unclip(points).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(np.int16)) scores.append(score) # try: # poly = contours[index] # # cv2.drawContours(debug_mat, poly, -1, (111, 90, 255), -1) # # epsilon = 0.001 * cv2.arcLength(poly, True) # approx = cv2.approxPolyDP(poly, epsilon, True) # points = approx.reshape((-1, 2)) # if points.shape[0] < 4: # continue # score = self.box_score_fast(pred, points) # if score < self.box_thresh: # continue # poly = self.unclip(points) # if len(poly) == 0 or isinstance(poly[0], list): # continue # poly = poly.reshape(-1, 2) # # # box, sside = self.get_mini_boxes(poly) # # if sside < self.min_size + 2: # # continue # # box = np.array(box) # box=np.array(poly) # # 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(np.int16).flatten().tolist()) # scores.append(score) # except: # print('1') # pass return boxes, scores def unclip(self, box): unclip_ratio = self.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): try: rotated_box = cv2.minAreaRect(contour) except: print(len(contour)) return None, 0 box_points = cv2.boxPoints(rotated_box) rotated_points = clockwise_sort_points(box_points) rotated_points = list(rotated_points) return rotated_points, min(rotated_box[1]) def box_score_fast(self, bitmap, _box): h, w = bitmap.shape[:2] box = _box.copy() xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1) xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1) ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1) ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1) 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(np.int32), 1) return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] def box_score_slow(self, bitmap, contour): ''' 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(np.int32), 1) return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] def __call__(self, outs_dict, shape_list): pred = outs_dict pred = pred[:, 0, :, :] segmentation = np.zeros_like(pred, dtype=np.float32) np.putmask(segmentation, pred > self.thresh, pred) boxes_batch = [] scores_batch = [] for batch_index in range(pred.shape[0]): src_h, src_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] boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, src_w, src_h, ) boxes_batch.append(boxes) scores_batch.append(scores) return boxes_batch, scores_batch