297 lines
11 KiB
Python
297 lines
11 KiB
Python
import copy
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from typing import Optional
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from typing import Tuple
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import math
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def point_to_line_distance(px, py, x1, y1, x2, y2):
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"""
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计算一个点，到另外两个点形成的直线上的垂直距离。（点到直线的距离公式）
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返回距离值
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"""
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# 分子部分
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numerator = abs((y2 - y1) * px - (x2 - x1) * py + x2 * y1 - y2 * x1)
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# 分母部分
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denominator = math.hypot(y2 - y1, x2 - x1)
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# 距离
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return numerator / denominator
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def from_main_get_other_point_distance(main_ymj, other_ymj):
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"""
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以main_ymj(主要预埋件)的四个边的中点。
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高度中点连线为x轴
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宽度中点连线为y轴
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计算other_ymj(其他预埋件)的中心点到主要预埋件x轴直线和y轴直线的的垂直距离
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返回x垂直距离,y垂直距离
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"""
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# 其他预埋件的中心点坐标
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px = other_ymj['x_center']
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py = other_ymj['y_center']
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# 计算其他预埋件中心点坐标到预埋件左右边中点坐标连线形成的直线的垂直距离
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bottom_line_center_x = main_ymj['bottom_line_center_x']
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bottom_line_center_y = main_ymj['bottom_line_center_y']
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top_line_center_x = main_ymj['top_line_center_x']
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top_line_center_y = main_ymj['top_line_center_y']
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x_distance = point_to_line_distance(px, py, bottom_line_center_x, bottom_line_center_y, top_line_center_x,
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top_line_center_y)
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# 计算其他预埋件中心点坐标到预埋件左右边中点坐标连线形成的直线的垂直距离
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left_line_center_x = main_ymj['left_line_center_x']
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left_line_center_y = main_ymj['left_line_center_y']
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right_line_center_x = main_ymj['right_line_center_x']
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right_line_center_y = main_ymj['right_line_center_y']
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y_distance = point_to_line_distance(px, py, left_line_center_x, left_line_center_y, right_line_center_x,
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right_line_center_y)
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return round(x_distance, 2), round(y_distance, 2)
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def from_main_get_other_point_distance_bim(main_ymj, other_ymj):
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"""
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与from_main_get_other_point_distance函数类似
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只不过计算的是bim图中，其他预埋件相对于主要预埋件的x和y的距离
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因为bim是标准的平面图，所以可以直接使用主要预埋件的中心点作为坐标原点，直接计算其他预埋件相对于这个坐标原点的x,y,
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返回x垂直距离,y垂直距离
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"""
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main_x = main_ymj["x"]
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main_x = float(main_x)
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main_y = main_ymj["center"]
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main_y = float(main_y) * 1000
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other_x = other_ymj["x"]
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other_x = float(other_x)
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other_y = other_ymj["center"]
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other_y = float(other_y) * 1000
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x_distance = abs(other_x - main_x)
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y_distance = abs(other_y - main_y)
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return round(x_distance, 2), round(y_distance, 2)
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def cal_deviation(main_ymj, other_ymj_list):
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"""
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计算偏差表
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bim和图片中其他预埋件中心点到主要预埋件的偏差值
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中心x轴直线偏差值
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中心y轴直线的的垂直距离偏差值
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欧几里德距离偏差值
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@:return
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[
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{
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'ymj_code': 预埋件code
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'x_deviation': x偏差
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'y_deviation': y偏差
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'euclidean_deviation': 欧几里德距离的偏差
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'average_deviation' : 平均误差
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'x_deviation_s': x偏差值，带正负符号
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'y_deviation_s': y偏差值，带正负符号
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}
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]
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"""
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# 1.计算图片和bim中，其他预埋件中心点到主要预埋件中心x轴直线和中心y轴直线的的垂直距离
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distance_data = [] # bim 和 图片中其他预埋件中心点到主要预埋件中心x轴直线和中心y轴直线的的垂直距离，欧几里德距离
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for other_ymj in other_ymj_list:
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# 计算图片中其他预埋件中心点到主要预埋件中心x轴直线和中心y轴直线的的垂直距离
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x_distance_pic, y_distance_pic = from_main_get_other_point_distance(main_ymj, other_ymj)
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pic_data = {
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'x_distance': x_distance_pic,
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'y_distance': y_distance_pic,
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# 欧几里得距离值
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'euclidean_distance': math.sqrt(x_distance_pic ** 2 + y_distance_pic ** 2),
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}
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# 计算bim中其他预埋件中心点到主要预埋件中心x轴直线和中心y轴直线的的垂直距离
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x_distance_bim, y_distance_bim = from_main_get_other_point_distance_bim(main_ymj, other_ymj)
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bim_data = {
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'x_distance': x_distance_bim,
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'y_distance': y_distance_bim,
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# 欧几里得距离值
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'euclidean_distance': math.sqrt(x_distance_bim ** 2 + y_distance_bim ** 2),
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}
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distance_data.append(
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{
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'code': other_ymj['code'],
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'pic_data': pic_data, # 图片中其他预埋件中心点到主要预埋件中心x轴直线和中心y轴直线的的垂直距离, 欧几里得距离值
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'bim_data': bim_data, # bim中其他预埋件中心点到主要预埋件中心x轴直线和中心y轴直线的的垂直距离, 欧几里得距离值
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}
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)
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# 2.对比bim和图片中数据的偏差值
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deviation_data = [] # 偏差值列表
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for distance in distance_data:
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pic_data = distance['pic_data']
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bim_data = distance['bim_data']
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ymj_code = distance['code']
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# 计算x偏差
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x_deviation = pic_data['x_distance'] - bim_data['x_distance']
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# 计算y偏差
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y_deviation = pic_data['y_distance'] - bim_data['y_distance']
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# 计算欧几里得距离偏差
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sqrt_xx_yy_deviation = pic_data['euclidean_distance'] - bim_data['euclidean_distance']
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# 计算平均误差
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average_deviation = max(abs(x_deviation), abs(y_deviation), abs(sqrt_xx_yy_deviation))
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deviation_data.append({
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'ymj_code': ymj_code,
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'x_deviation': abs(x_deviation),
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'x_deviation_s': x_deviation,
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'y_deviation': abs(y_deviation),
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'y_deviation_s': y_deviation,
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'euclidean_deviation': abs(sqrt_xx_yy_deviation),
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'average_deviation': average_deviation,
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})
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# 3.返回计算偏差值列表
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return deviation_data
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def get_point_align_line(
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px: float,
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py: float,
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x_axis: Optional[Tuple[float, float, float, float]],
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y_axis: Optional[Tuple[float, float, float, float]],
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x_move_distance: float,
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y_move_distance: float
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) -> Tuple[float, float]:
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"""
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计算一点沿着两点形成的直线方向，移动指定距离之后的坐标值。
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注意方向为：
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起点 x_axis_x1,x_axis_y1 ==> 终点 x_axis_x2,x_axis_y2
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起点 y_axis_x1,y_axis_y1 ==> 终点 y_axis_x2,y_axis_y2
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相当于 px, py 沿着 x_axis 和 y_axis 的方向各移动 x_move_distance 和 y_move_distance。
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:return: 移动后的新坐标 (new_x, new_y)
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"""
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new_x, new_y = px, py
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# 如果 x 方向有位移
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if x_move_distance != 0:
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x1, y1, x2, y2 = x_axis
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dx = x2 - x1
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dy = y2 - y1
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length = math.hypot(dx, dy)
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if length != 0:
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x_unit_vec = (dx / length, dy / length)
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new_x += x_move_distance * x_unit_vec[0]
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new_y += x_move_distance * x_unit_vec[1]
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# 如果 y 方向有位移
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if y_move_distance != 0:
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x1, y1, x2, y2 = y_axis
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dx = x2 - x1
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dy = y2 - y1
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length = math.hypot(dx, dy)
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if length != 0:
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y_unit_vec = (dx / length, dy / length)
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new_x += y_move_distance * y_unit_vec[0]
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new_y += y_move_distance * y_unit_vec[1]
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return new_x, new_y
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def cal_deviation_table(all_ymj_data):
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"""
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计算每个预埋件的平均偏差表，
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:param all_ymj_data:
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:return: [
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{
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'ymj_code': 主要预埋件
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'average_deviation': 平均误差
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}
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]
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"""
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deviation_table = [] # 数值偏差表
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for main_ymj in all_ymj_data:
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other_ymj_list = copy.deepcopy(all_ymj_data)
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other_ymj_list.remove(main_ymj) # 移除主要预埋件
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# 1.计算偏差数据
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deviation_data = cal_deviation(main_ymj, other_ymj_list)
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print(f"main_ymj_code={main_ymj['code']}")
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print(f"deviation_data={deviation_data}")
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# 2.计算所有其他预埋件误差的平均值
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all_average_deviation = 0
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for deviation in deviation_data:
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# 一个预埋件的误差平均值
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average_deviation = deviation['average_deviation']
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# 误差 累加起来
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all_average_deviation += average_deviation
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# 除以预埋件数量
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all_average_deviation = all_average_deviation / len(deviation_data)
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deviation_table.append({
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'ymj_code': main_ymj['code'],
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'average_deviation': all_average_deviation,
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})
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print(f"📋deviation_table={deviation_table}")
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return deviation_table
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def cal_with_manual_measurement(all_ymj_data):
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"""
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根据人工测量出来的偏差值，计算预埋件偏差值
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@:return
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main_ymj_code : 使用了人工测量数据的预埋件
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measure_deviation: 其他预埋件相当于人工测量出数据的实际偏差
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"""
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main_ymj_code = None
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measure_deviation = []
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# 深度拷贝一份全部预埋件数据, 防止源数据被修改
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all_ymj_data_copy = copy.deepcopy(all_ymj_data)
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# 遍历查找哪个数据有人工测量的值
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is_find = False
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for idx, ymj in enumerate(all_ymj_data_copy):
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# 如果有人工测量数据
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if 'x_measure_bias' in ymj:
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is_find = True
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x_measure_bias = ymj['x_measure_bias'] # x偏差
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y_measure_bias = ymj['y_measure_bias'] # y偏差
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# 先获取其他预埋件相对于这个预埋件的偏差
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other_ymj_list = all_ymj_data_copy
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other_ymj_list.remove(ymj)
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deviation_data = cal_deviation(ymj, other_ymj_list)
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print(f"主预埋件code={ymj['code']}")
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print(f"其他预埋件偏差={deviation_data}")
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# 给其他预埋件的偏差值 加上人工测量的偏差 以获得其真实偏差
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for deviation in deviation_data:
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# 带符号的偏差偏差值
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x_deviation_s = deviation['x_deviation_s']
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y_deviation_s = deviation['y_deviation_s']
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# 找到这个预埋件的源数据
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for ymj_data in other_ymj_list:
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# code相同判断
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if ymj_data['code'] == deviation['ymj_code']:
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# 偏差值再加上人工测量的偏差 以获得其真实偏差
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measure_deviation.append({
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'ymj_code': deviation['ymj_code'],
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'x_deviation': x_deviation_s + x_measure_bias,
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'y_deviation': y_deviation_s + y_measure_bias,
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})
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break
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if is_find:
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# 只拿取第一个人工测量的预埋件的数据
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main_ymj_code = ymj['code']
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break
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if not is_find:
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print("❗️❗️没有任何一个预埋件存在人工测量的数据")
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return main_ymj_code,measure_deviation
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