# -*- encoding: utf-8 -*- # @Author: SWHL # @Contact: liekkaskono@163.com import copy import math import cv2 import numpy as np from scipy.spatial import distance as dist from skimage import measure def bbox_decode(heat, wh, reg=None, K=100): """bbox组成:[V1, V2, V3, V4] V1~V4: bbox的4个坐标点 """ batch = heat.shape[0] heat, keep = _nms(heat) scores, inds, clses, ys, xs = _topk(heat, K=K) if reg is not None: reg = _tranpose_and_gather_feat(reg, inds) reg = reg.reshape(batch, K, 2) xs = xs.reshape(batch, K, 1) + reg[:, :, 0:1] ys = ys.reshape(batch, K, 1) + reg[:, :, 1:2] else: xs = xs.reshape(batch, K, 1) + 0.5 ys = ys.reshape(batch, K, 1) + 0.5 wh = _tranpose_and_gather_feat(wh, inds) wh = wh.reshape(batch, K, 8) clses = clses.reshape(batch, K, 1).astype(np.float32) scores = scores.reshape(batch, K, 1) bboxes = np.concatenate( [ xs - wh[..., 0:1], ys - wh[..., 1:2], xs - wh[..., 2:3], ys - wh[..., 3:4], xs - wh[..., 4:5], ys - wh[..., 5:6], xs - wh[..., 6:7], ys - wh[..., 7:8], ], axis=2, ) detections = np.concatenate([bboxes, scores, clses], axis=2) return detections, inds def _nms(heat, kernel=3): pad = (kernel - 1) // 2 hmax = max_pool(heat, kernel_size=kernel, stride=1, padding=pad) keep = hmax == heat return heat * keep, keep def max_pool(img, kernel_size, stride, padding): h, w = img.shape[2:] img = np.pad( img, ((0, 0), (0, 0), (padding, padding), (padding, padding)), "constant", constant_values=0, ) res_h = ((h + 2 - kernel_size) // stride) + 1 res_w = ((w + 2 - kernel_size) // stride) + 1 res = np.zeros((img.shape[0], img.shape[1], res_h, res_w)) for i in range(res_h): for j in range(res_w): temp = img[ :, :, i * stride : i * stride + kernel_size, j * stride : j * stride + kernel_size, ] res[:, :, i, j] = temp.max() return res def _topk(scores, K=40): batch, cat, height, width = scores.shape topk_scores, topk_inds = find_topk(scores.reshape(batch, cat, -1), K) topk_inds = topk_inds % (height * width) topk_ys = topk_inds / width topk_xs = np.float32(np.int32(topk_inds % width)) topk_score, topk_ind = find_topk(topk_scores.reshape(batch, -1), K) topk_clses = np.int32(topk_ind / K) topk_inds = _gather_feat(topk_inds.reshape(batch, -1, 1), topk_ind).reshape( batch, K ) topk_ys = _gather_feat(topk_ys.reshape(batch, -1, 1), topk_ind).reshape(batch, K) topk_xs = _gather_feat(topk_xs.reshape(batch, -1, 1), topk_ind).reshape(batch, K) return topk_score, topk_inds, topk_clses, topk_ys, topk_xs def find_topk(a, k, axis=-1, largest=True, sorted=True): if axis is None: axis_size = a.size else: axis_size = a.shape[axis] assert 1 <= k <= axis_size a = np.asanyarray(a) if largest: index_array = np.argpartition(a, axis_size - k, axis=axis) topk_indices = np.take(index_array, -np.arange(k) - 1, axis=axis) else: index_array = np.argpartition(a, k - 1, axis=axis) topk_indices = np.take(index_array, np.arange(k), axis=axis) topk_values = np.take_along_axis(a, topk_indices, axis=axis) if sorted: sorted_indices_in_topk = np.argsort(topk_values, axis=axis) if largest: sorted_indices_in_topk = np.flip(sorted_indices_in_topk, axis=axis) sorted_topk_values = np.take_along_axis( topk_values, sorted_indices_in_topk, axis=axis ) sorted_topk_indices = np.take_along_axis( topk_indices, sorted_indices_in_topk, axis=axis ) return sorted_topk_values, sorted_topk_indices return topk_values, topk_indices def _gather_feat(feat, ind): dim = feat.shape[2] ind = np.broadcast_to(ind[:, :, None], (ind.shape[0], ind.shape[1], dim)) feat = _gather_np(feat, 1, ind) return feat def _gather_np(data, dim, index): """ Gathers values along an axis specified by dim. For a 3-D tensor the output is specified by: out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0 out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1 out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2 :param dim: The axis along which to index :param index: A tensor of indices of elements to gather :return: tensor of gathered values """ idx_xsection_shape = index.shape[:dim] + index.shape[dim + 1 :] data_xsection_shape = data.shape[:dim] + data.shape[dim + 1 :] if idx_xsection_shape != data_xsection_shape: raise ValueError( "Except for dimension " + str(dim) + ", all dimensions of index and data should be the same size" ) if index.dtype != np.int64: raise TypeError("The values of index must be integers") data_swaped = np.swapaxes(data, 0, dim) index_swaped = np.swapaxes(index, 0, dim) gathered = np.take_along_axis(data_swaped, index_swaped, axis=0) return np.swapaxes(gathered, 0, dim) def _tranpose_and_gather_feat(feat, ind): feat = np.ascontiguousarray(np.transpose(feat, [0, 2, 3, 1])) feat = feat.reshape(feat.shape[0], -1, feat.shape[3]) feat = _gather_feat(feat, ind) return feat def gbox_decode(mk, st_reg, reg=None, K=400): """gbox的组成:[V1, P1, P2, P3, P4] P1~P4: 四个框的中心点 V1: 四个框的交点 """ batch = mk.shape[0] mk, keep = _nms(mk) scores, inds, clses, ys, xs = _topk(mk, K=K) if reg is not None: reg = _tranpose_and_gather_feat(reg, inds) reg = reg.reshape(batch, K, 2) xs = xs.reshape(batch, K, 1) + reg[:, :, 0:1] ys = ys.reshape(batch, K, 1) + reg[:, :, 1:2] else: xs = xs.reshape(batch, K, 1) + 0.5 ys = ys.reshape(batch, K, 1) + 0.5 scores = scores.reshape(batch, K, 1) clses = clses.reshape(batch, K, 1).astype(np.float32) st_Reg = _tranpose_and_gather_feat(st_reg, inds) bboxes = np.concatenate( [ xs - st_Reg[..., 0:1], ys - st_Reg[..., 1:2], xs - st_Reg[..., 2:3], ys - st_Reg[..., 3:4], xs - st_Reg[..., 4:5], ys - st_Reg[..., 5:6], xs - st_Reg[..., 6:7], ys - st_Reg[..., 7:8], ], axis=2, ) return np.concatenate([xs, ys, bboxes, scores, clses], axis=2), keep def transform_preds(coords, center, scale, output_size, rot=0): target_coords = np.zeros(coords.shape) trans = get_affine_transform(center, scale, rot, output_size, inv=1) for p in range(coords.shape[0]): target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) return target_coords def get_affine_transform( center, scale, rot, output_size, shift=np.array([0, 0], dtype=np.float32), inv=0 ): if not isinstance(scale, np.ndarray) and not isinstance(scale, list): scale = np.array([scale, scale], dtype=np.float32) scale_tmp = scale src_w = scale_tmp[0] dst_w = output_size[0] dst_h = output_size[1] rot_rad = np.pi * rot / 180 src_dir = get_dir([0, src_w * -0.5], rot_rad) dst_dir = np.array([0, dst_w * -0.5], np.float32) src = np.zeros((3, 2), dtype=np.float32) dst = np.zeros((3, 2), dtype=np.float32) src[0, :] = center + scale_tmp * shift src[1, :] = center + src_dir + scale_tmp * shift dst[0, :] = [dst_w * 0.5, dst_h * 0.5] dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5], np.float32) + dst_dir src[2:, :] = get_3rd_point(src[0, :], src[1, :]) dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) if inv: trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) else: trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) return trans def affine_transform(pt, t): new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32).T new_pt = np.dot(t, new_pt) return new_pt[:2] def get_dir(src_point, rot_rad): sn, cs = np.sin(rot_rad), np.cos(rot_rad) src_result = [0, 0] src_result[0] = src_point[0] * cs - src_point[1] * sn src_result[1] = src_point[0] * sn + src_point[1] * cs return src_result def get_3rd_point(a, b): direct = a - b return b + np.array([-direct[1], direct[0]], dtype=np.float32) def bbox_post_process(bbox, c, s, h, w): for i in range(bbox.shape[0]): bbox[i, :, 0:2] = transform_preds(bbox[i, :, 0:2], c[i], s[i], (w, h)) bbox[i, :, 2:4] = transform_preds(bbox[i, :, 2:4], c[i], s[i], (w, h)) bbox[i, :, 4:6] = transform_preds(bbox[i, :, 4:6], c[i], s[i], (w, h)) bbox[i, :, 6:8] = transform_preds(bbox[i, :, 6:8], c[i], s[i], (w, h)) return bbox def gbox_post_process(gbox, c, s, h, w): for i in range(gbox.shape[0]): gbox[i, :, 0:2] = transform_preds(gbox[i, :, 0:2], c[i], s[i], (w, h)) gbox[i, :, 2:4] = transform_preds(gbox[i, :, 2:4], c[i], s[i], (w, h)) gbox[i, :, 4:6] = transform_preds(gbox[i, :, 4:6], c[i], s[i], (w, h)) gbox[i, :, 6:8] = transform_preds(gbox[i, :, 6:8], c[i], s[i], (w, h)) gbox[i, :, 8:10] = transform_preds(gbox[i, :, 8:10], c[i], s[i], (w, h)) return gbox def nms(dets, thresh): if len(dets) < 2: return dets index_keep, keep = [], [] for i in range(len(dets)): box = dets[i] if box[-1] < thresh: break max_score_index = -1 ctx = (dets[i][0] + dets[i][2] + dets[i][4] + dets[i][6]) / 4 cty = (dets[i][1] + dets[i][3] + dets[i][5] + dets[i][7]) / 4 for j in range(len(dets)): if i == j or dets[j][-1] < thresh: break x1, y1 = dets[j][0], dets[j][1] x2, y2 = dets[j][2], dets[j][3] x3, y3 = dets[j][4], dets[j][5] x4, y4 = dets[j][6], dets[j][7] a = (x2 - x1) * (cty - y1) - (y2 - y1) * (ctx - x1) b = (x3 - x2) * (cty - y2) - (y3 - y2) * (ctx - x2) c = (x4 - x3) * (cty - y3) - (y4 - y3) * (ctx - x3) d = (x1 - x4) * (cty - y4) - (y1 - y4) * (ctx - x4) if all(x > 0 for x in (a, b, c, d)) or all(x < 0 for x in (a, b, c, d)): if dets[i][8] > dets[j][8] and max_score_index < 0: max_score_index = i elif dets[i][8] < dets[j][8]: max_score_index = -2 break if max_score_index > -1: index_keep.append(max_score_index) elif max_score_index == -1: index_keep.append(i) keep = [dets[index_keep[i]] for i in range(len(index_keep))] return np.array(keep) def group_bbox_by_gbox( bboxes, gboxes, score_thred=0.3, v2c_dist_thred=2, c2v_dist_thred=0.5 ): def point_in_box(box, point): x1, y1, x2, y2 = box[0], box[1], box[2], box[3] x3, y3, x4, y4 = box[4], box[5], box[6], box[7] ctx, cty = point[0], point[1] a = (x2 - x1) * (cty - y1) - (y2 - y1) * (ctx - x1) b = (x3 - x2) * (cty - y2) - (y3 - y2) * (ctx - x2) c = (x4 - x3) * (cty - y3) - (y4 - y3) * (ctx - x3) d = (x1 - x4) * (cty - y4) - (y1 - y4) * (ctx - x4) if all(x > 0 for x in (a, b, c, d)) or all(x < 0 for x in (a, b, c, d)): return True return False def get_distance(pt1, pt2): return math.sqrt( (pt1[0] - pt2[0]) * (pt1[0] - pt2[0]) + (pt1[1] - pt2[1]) * (pt1[1] - pt2[1]) ) dets = copy.deepcopy(bboxes) sign = np.zeros((len(dets), 4)) for gbox in gboxes: if gbox[10] < score_thred: break vertex = [gbox[0], gbox[1]] for i in range(4): center = [gbox[2 * i + 2], gbox[2 * i + 3]] if get_distance(vertex, center) < v2c_dist_thred: continue for k, bbox in enumerate(dets): if bbox[8] < score_thred: break if sum(sign[k]) == 4: continue w = (abs(bbox[6] - bbox[0]) + abs(bbox[4] - bbox[2])) / 2 h = (abs(bbox[3] - bbox[1]) + abs(bbox[5] - bbox[7])) / 2 m = max(w, h) if point_in_box(bbox, center): min_dist, min_id = 1e4, -1 for j in range(4): dist = get_distance(vertex, [bbox[2 * j], bbox[2 * j + 1]]) if dist < min_dist: min_dist = dist min_id = j if ( min_id > -1 and min_dist < c2v_dist_thred * m and sign[k][min_id] == 0 ): bboxes[k][2 * min_id] = vertex[0] bboxes[k][2 * min_id + 1] = vertex[1] sign[k][min_id] = 1 return bboxes def get_table_line(binimg, axis=0, lineW=10): ##获取表格线 ##axis=0 横线 ##axis=1 竖线 labels = measure.label(binimg > 0, connectivity=2) # 8连通区域标记 regions = measure.regionprops(labels) if axis == 1: lineboxes = [ min_area_rect(line.coords) for line in regions if line.bbox[2] - line.bbox[0] > lineW ] else: lineboxes = [ min_area_rect(line.coords) for line in regions if line.bbox[3] - line.bbox[1] > lineW ] return lineboxes def min_area_rect(coords): """ 多边形外接矩形 """ rect = cv2.minAreaRect(coords[:, ::-1]) box = cv2.boxPoints(rect) box = box.reshape((8,)).tolist() box = image_location_sort_box(box) x1, y1, x2, y2, x3, y3, x4, y4 = box degree, w, h, cx, cy = calculate_center_rotate_angle(box) if w < h: xmin = (x1 + x2) / 2 xmax = (x3 + x4) / 2 ymin = (y1 + y2) / 2 ymax = (y3 + y4) / 2 else: xmin = (x1 + x4) / 2 xmax = (x2 + x3) / 2 ymin = (y1 + y4) / 2 ymax = (y2 + y3) / 2 # degree,w,h,cx,cy = solve(box) # x1,y1,x2,y2,x3,y3,x4,y4 = box # return {'degree':degree,'w':w,'h':h,'cx':cx,'cy':cy} return [xmin, ymin, xmax, ymax] def image_location_sort_box(box): x1, y1, x2, y2, x3, y3, x4, y4 = box[:8] pts = (x1, y1), (x2, y2), (x3, y3), (x4, y4) pts = np.array(pts, dtype="float32") (x1, y1), (x2, y2), (x3, y3), (x4, y4) = _order_points(pts) return [x1, y1, x2, y2, x3, y3, x4, y4] def calculate_center_rotate_angle(box): """ 绕 cx,cy点 w,h 旋转 angle 的坐标,能一定程度缓解图片的内部倾斜,但是还是依赖模型稳妥 x = cx-w/2 y = cy-h/2 x1-cx = -w/2*cos(angle) +h/2*sin(angle) y1 -cy= -w/2*sin(angle) -h/2*cos(angle) h(x1-cx) = -wh/2*cos(angle) +hh/2*sin(angle) w(y1 -cy)= -ww/2*sin(angle) -hw/2*cos(angle) (hh+ww)/2sin(angle) = h(x1-cx)-w(y1 -cy) """ x1, y1, x2, y2, x3, y3, x4, y4 = box[:8] cx = (x1 + x3 + x2 + x4) / 4.0 cy = (y1 + y3 + y4 + y2) / 4.0 w = ( np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + np.sqrt((x3 - x4) ** 2 + (y3 - y4) ** 2) ) / 2 h = ( np.sqrt((x2 - x3) ** 2 + (y2 - y3) ** 2) + np.sqrt((x1 - x4) ** 2 + (y1 - y4) ** 2) ) / 2 # x = cx-w/2 # y = cy-h/2 sinA = (h * (x1 - cx) - w * (y1 - cy)) * 1.0 / (h * h + w * w) * 2 angle = np.arcsin(sinA) return angle, w, h, cx, cy def _order_points(pts): # 根据x坐标对点进行排序 """ --------------------- 本项目中是为了排序后得到[(xmin,ymin),(xmax,ymin),(xmax,ymax),(xmin,ymax)] 作者:Tong_T 来源:CSDN 原文:https://blog.csdn.net/Tong_T/article/details/81907132 版权声明:本文为博主原创文章,转载请附上博文链接! """ x_sorted = pts[np.argsort(pts[:, 0]), :] left_most = x_sorted[:2, :] right_most = x_sorted[2:, :] left_most = left_most[np.argsort(left_most[:, 1]), :] (tl, bl) = left_most distance = dist.cdist(tl[np.newaxis], right_most, "euclidean")[0] (br, tr) = right_most[np.argsort(distance)[::-1], :] return np.array([tl, tr, br, bl], dtype="float32") def sqrt(p1, p2): return np.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2) def adjust_lines(lines, alph=50, angle=50): lines_n = len(lines) new_lines = [] for i in range(lines_n): x1, y1, x2, y2 = lines[i] cx1, cy1 = (x1 + x2) / 2, (y1 + y2) / 2 for j in range(lines_n): if i != j: x3, y3, x4, y4 = lines[j] cx2, cy2 = (x3 + x4) / 2, (y3 + y4) / 2 if (x3 < cx1 < x4 or y3 < cy1 < y4) or ( x1 < cx2 < x2 or y1 < cy2 < y2 ): # 判断两个横线在y方向的投影重不重合 continue else: r = sqrt((x1, y1), (x3, y3)) k = abs((y3 - y1) / (x3 - x1 + 1e-10)) a = math.atan(k) * 180 / math.pi if r < alph and a < angle: new_lines.append((x1, y1, x3, y3)) r = sqrt((x1, y1), (x4, y4)) k = abs((y4 - y1) / (x4 - x1 + 1e-10)) a = math.atan(k) * 180 / math.pi if r < alph and a < angle: new_lines.append((x1, y1, x4, y4)) r = sqrt((x2, y2), (x3, y3)) k = abs((y3 - y2) / (x3 - x2 + 1e-10)) a = math.atan(k) * 180 / math.pi if r < alph and a < angle: new_lines.append((x2, y2, x3, y3)) r = sqrt((x2, y2), (x4, y4)) k = abs((y4 - y2) / (x4 - x2 + 1e-10)) a = math.atan(k) * 180 / math.pi if r < alph and a < angle: new_lines.append((x2, y2, x4, y4)) return new_lines def final_adjust_lines(rowboxes, colboxes): nrow = len(rowboxes) ncol = len(colboxes) for i in range(nrow): for j in range(ncol): rowboxes[i] = line_to_line(rowboxes[i], colboxes[j], alpha=20, angle=30) colboxes[j] = line_to_line(colboxes[j], rowboxes[i], alpha=20, angle=30) return rowboxes, colboxes def draw_lines(im, bboxes, color=(0, 0, 0), lineW=3): """ boxes: bounding boxes """ tmp = np.copy(im) c = color h, w = im.shape[:2] for box in bboxes: x1, y1, x2, y2 = box[:4] cv2.line( tmp, (int(x1), int(y1)), (int(x2), int(y2)), c, lineW, lineType=cv2.LINE_AA ) return tmp def line_to_line(points1, points2, alpha=10, angle=30): """ 线段之间的距离 """ x1, y1, x2, y2 = points1 ox1, oy1, ox2, oy2 = points2 xy = np.array([(x1, y1), (x2, y2)], dtype="float32") A1, B1, C1 = fit_line(xy) oxy = np.array([(ox1, oy1), (ox2, oy2)], dtype="float32") A2, B2, C2 = fit_line(oxy) flag1 = point_line_cor(np.array([x1, y1], dtype="float32"), A2, B2, C2) flag2 = point_line_cor(np.array([x2, y2], dtype="float32"), A2, B2, C2) if (flag1 > 0 and flag2 > 0) or (flag1 < 0 and flag2 < 0): # 横线或者竖线在竖线或者横线的同一侧 if (A1 * B2 - A2 * B1) != 0: x = (B1 * C2 - B2 * C1) / (A1 * B2 - A2 * B1) y = (A2 * C1 - A1 * C2) / (A1 * B2 - A2 * B1) # x, y = round(x, 2), round(y, 2) p = (x, y) # 横线与竖线的交点 r0 = sqrt(p, (x1, y1)) r1 = sqrt(p, (x2, y2)) if min(r0, r1) < alpha: # 若交点与线起点或者终点的距离小于alpha,则延长线到交点 if r0 < r1: k = abs((y2 - p[1]) / (x2 - p[0] + 1e-10)) a = math.atan(k) * 180 / math.pi if a < angle or abs(90 - a) < angle: points1 = np.array([p[0], p[1], x2, y2], dtype="float32") else: k = abs((y1 - p[1]) / (x1 - p[0] + 1e-10)) a = math.atan(k) * 180 / math.pi if a < angle or abs(90 - a) < angle: points1 = np.array([x1, y1, p[0], p[1]], dtype="float32") return points1 def min_area_rect_box( regions, flag=True, W=0, H=0, filtersmall=False, adjust_box=False ): """ 多边形外接矩形 """ boxes = [] for region in regions: if region.bbox_area > H * W * 3 / 4: # 过滤大的单元格 continue rect = cv2.minAreaRect(region.coords[:, ::-1]) box = cv2.boxPoints(rect) box = box.reshape((8,)).tolist() box = image_location_sort_box(box) x1, y1, x2, y2, x3, y3, x4, y4 = box angle, w, h, cx, cy = calculate_center_rotate_angle(box) # if adjustBox: # x1, y1, x2, y2, x3, y3, x4, y4 = xy_rotate_box(cx, cy, w + 5, h + 5, angle=0, degree=None) # x1, x4 = max(x1, 0), max(x4, 0) # y1, y2 = max(y1, 0), max(y2, 0) # if w > 32 and h > 32 and flag: # if abs(angle / np.pi * 180) < 20: # if filtersmall and (w < 10 or h < 10): # continue # boxes.append([x1, y1, x2, y2, x3, y3, x4, y4]) # else: if w * h < 0.5 * W * H: if filtersmall and ( w < 15 or h < 15 ): # or w / h > 30 or h / w > 30): # 过滤小的单元格 continue boxes.append([x1, y1, x2, y2, x3, y3, x4, y4]) return boxes def point_line_cor(p, A, B, C): ##判断点与线之间的位置关系 # 一般式直线方程(Ax+By+c)=0 x, y = p r = A * x + B * y + C return r def fit_line(p): """A = Y2 - Y1 B = X1 - X2 C = X2*Y1 - X1*Y2 AX+BY+C=0 直线一般方程 """ x1, y1 = p[0] x2, y2 = p[1] A = y2 - y1 B = x1 - x2 C = x2 * y1 - x1 * y2 return A, B, C