import json def distance(box_1, box_2): x1, y1, x2, y2 = box_1 x3, y3, x4, y4 = box_2 # min_x = (x1 + x2) / 2 # min_y = (y1 + y2) / 2 # max_x = (x3 + x4) / 2 # max_y = (y3 + y4) / 2 dis = abs(x3 - x1) + abs(y3 - y1) + abs(x4- x2) + abs(y4 - y2) dis_2 = abs(x3 - x1) + abs(y3 - y1) dis_3 = abs(x4- x2) + abs(y4 - y2) #dis = pow(min_x - max_x, 2) + pow(min_y - max_y, 2) + pow(x3 - x1, 2) + pow(y3 - y1, 2) + pow(x4- x2, 2) + pow(y4 - y2, 2) + abs(x3 - x1) + abs(y3 - y1) + abs(x4- x2) + abs(y4 - y2) return dis + min(dis_2, dis_3) def compute_iou(rec1, rec2): """ computing IoU :param rec1: (y0, x0, y1, x1), which reflects (top, left, bottom, right) :param rec2: (y0, x0, y1, x1) :return: scala value of IoU """ # computing area of each rectangles rec1, rec2 = rec1 * 1000, rec2 * 1000 S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1]) S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1]) # computing the sum_area sum_area = S_rec1 + S_rec2 # find the each edge of intersect rectangle left_line = max(rec1[1], rec2[1]) right_line = min(rec1[3], rec2[3]) top_line = max(rec1[0], rec2[0]) bottom_line = min(rec1[2], rec2[2]) # judge if there is an intersect if left_line >= right_line or top_line >= bottom_line: return 0 else: intersect = (right_line - left_line) * (bottom_line - top_line) return (intersect / (sum_area - intersect))*1.0 def matcher_merge(ocr_bboxes, pred_bboxes): # ocr_bboxes: OCR pred_bboxes：端到端 all_dis = [] ious = [] matched = {} for i, gt_box in enumerate(ocr_bboxes): distances = [] for j, pred_box in enumerate(pred_bboxes): distances.append((distance(gt_box, pred_box), 1. - compute_iou(gt_box, pred_box))) #获取两两cell之间的L1距离和 1- IOU sorted_distances = distances.copy() # 根据距离和IOU挑选最"近"的cell sorted_distances = sorted(sorted_distances, key = lambda item: (item[1], item[0])) if distances.index(sorted_distances[0]) not in matched.keys(): matched[distances.index(sorted_distances[0])] = [i] else: matched[distances.index(sorted_distances[0])].append(i) return matched#, sum(ious) / len(ious) def complex_num(pred_bboxes): complex_nums = [] for bbox in pred_bboxes: distances = [] temp_ious = [] for pred_bbox in pred_bboxes: if bbox != pred_bbox: distances.append(distance(bbox, pred_bbox)) temp_ious.append(compute_iou(bbox, pred_bbox)) complex_nums.append(temp_ious[distances.index(min(distances))]) return sum(complex_nums) / len(complex_nums) def get_rows(pred_bboxes): pre_bbox = pred_bboxes[0] res = [] step = 0 for i in range(len(pred_bboxes)): bbox = pred_bboxes[i] if bbox[1] - pre_bbox[1] > 2 or bbox[0] - pre_bbox[0] < 0: break else: res.append(bbox) step += 1 for i in range(step): pred_bboxes.pop(0) return res, pred_bboxes def refine_rows(pred_bboxes): # 微调整行的框，使在一条水平线上 ys_1 = [] ys_2 = [] for box in pred_bboxes: ys_1.append(box[1]) ys_2.append(box[3]) min_y_1 = sum(ys_1) / len(ys_1) min_y_2 = sum(ys_2) / len(ys_2) re_boxes = [] for box in pred_bboxes: box[1] = min_y_1 box[3] = min_y_2 re_boxes.append(box) return re_boxes def matcher_refine_row(gt_bboxes, pred_bboxes): before_refine_pred_bboxes = pred_bboxes.copy() pred_bboxes = [] while(len(before_refine_pred_bboxes) != 0): row_bboxes, before_refine_pred_bboxes = get_rows(before_refine_pred_bboxes) print(row_bboxes) pred_bboxes.extend(refine_rows(row_bboxes)) all_dis = [] ious = [] matched = {} for i, gt_box in enumerate(gt_bboxes): distances = [] #temp_ious = [] for j, pred_box in enumerate(pred_bboxes): distances.append(distance(gt_box, pred_box)) #temp_ious.append(compute_iou(gt_box, pred_box)) #all_dis.append(min(distances)) #ious.append(temp_ious[distances.index(min(distances))]) if distances.index(min(distances)) not in matched.keys(): matched[distances.index(min(distances))] = [i] else: matched[distances.index(min(distances))].append(i) return matched#, sum(ious) / len(ious) #先挑选出一行，再进行匹配 def matcher_structure_1(gt_bboxes, pred_bboxes_rows, pred_bboxes): gt_box_index = 0 delete_gt_bboxes = gt_bboxes.copy() match_bboxes_ready = [] matched = {} while(len(delete_gt_bboxes) != 0): row_bboxes, delete_gt_bboxes = get_rows(delete_gt_bboxes) row_bboxes = sorted(row_bboxes, key = lambda key: key[0]) if len(pred_bboxes_rows) > 0: match_bboxes_ready.extend(pred_bboxes_rows.pop(0)) print(row_bboxes) for i, gt_box in enumerate(row_bboxes): #print(gt_box) pred_distances = [] distances = [] for pred_bbox in pred_bboxes: pred_distances.append(distance(gt_box, pred_bbox)) for j, pred_box in enumerate(match_bboxes_ready): distances.append(distance(gt_box, pred_box)) index = pred_distances.index(min(distances)) #print('index', index) if index not in matched.keys(): matched[index] = [gt_box_index] else: matched[index].append(gt_box_index) gt_box_index += 1 return matched def matcher_structure(gt_bboxes, pred_bboxes_rows, pred_bboxes): ''' gt_bboxes: 排序后 pred_bboxes: ''' pre_bbox = gt_bboxes[0] matched = {} match_bboxes_ready = [] match_bboxes_ready.extend(pred_bboxes_rows.pop(0)) for i, gt_box in enumerate(gt_bboxes): pred_distances = [] for pred_bbox in pred_bboxes: pred_distances.append(distance(gt_box, pred_bbox)) distances = [] gap_pre = gt_box[1] - pre_bbox[1] gap_pre_1 = gt_box[0] - pre_bbox[2] #print(gap_pre, len(pred_bboxes_rows)) if (gap_pre_1 < 0 and len(pred_bboxes_rows) > 0): match_bboxes_ready.extend(pred_bboxes_rows.pop(0)) if len(pred_bboxes_rows) == 1: match_bboxes_ready.extend(pred_bboxes_rows.pop(0)) if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) > 0: match_bboxes_ready.extend(pred_bboxes_rows.pop(0)) if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) == 0: break #print(match_bboxes_ready) for j, pred_box in enumerate(match_bboxes_ready): distances.append(distance(gt_box, pred_box)) index = pred_distances.index(min(distances)) #print(gt_box, index) #match_bboxes_ready.pop(distances.index(min(distances))) print(gt_box, match_bboxes_ready[distances.index(min(distances))]) if index not in matched.keys(): matched[index] = [i] else: matched[index].append(i) pre_bbox = gt_box return matched