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- # -*- 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
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