185 lines
3.1 KiB
Python
185 lines
3.1 KiB
Python
# -*- coding: UTF-8 -*-
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#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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'''
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@File : 多边形等距缩放.py
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@data ：2021/7/5 15:53
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@Desciption :
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@Version :
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@License :
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'''
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import cv2
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import numpy as np
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def scale(data, sec_dis):
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"""多边形等距缩放
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Args:
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data: 多边形按照逆时针顺序排列的的点集
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sec_dis: 缩放距离
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Returns:
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缩放后的多边形点集
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"""
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num = len(data)
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scal_data = []
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for i in range(num):
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x1 = data[(i) % num][0] - data[(i - 1) % num][0]
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y1 = data[(i) % num][1] - data[(i - 1) % num][1]
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x2 = data[(i + 1) % num][0] - data[(i) % num][0]
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y2 = data[(i + 1) % num][1] - data[(i) % num][1]
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d_A = (x1 ** 2 + y1 ** 2) ** 0.5
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d_B = (x2 ** 2 + y2 ** 2) ** 0.5
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Vec_Cross = (x1 * y2) - (x2 * y1)
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if (d_A * d_B==0):
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continue
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sin_theta = Vec_Cross / (d_A * d_B)
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if (sin_theta==0):
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continue
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dv = sec_dis / sin_theta
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v1_x = (dv / d_A) * x1
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v1_y = (dv / d_A) * y1
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v2_x = (dv / d_B) * x2
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v2_y = (dv / d_B) * y2
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PQ_x = v1_x - v2_x
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PQ_y = v1_y - v2_y
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Q_x = data[(i) % num][0] + PQ_x
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Q_y = data[(i) % num][1] + PQ_y
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scal_data.append([Q_x, Q_y])
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return scal_data
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data = [[454 , 76],
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[448 ,78],
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[444, 81],
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[440 , 85],
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[438, 90],
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[437, 96],
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[436 ,101],
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[434, 107],
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[432 ,112],
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[431 ,117],
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[430 ,123],
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[429, 129],
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[428, 134],
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[427, 140],
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[427, 145],
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[427, 151],
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[427 ,157],
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[427 ,163],
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[427, 169],
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[427, 175],
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[427, 181],
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[427, 187],
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[428, 193],
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[428 ,199],
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[429, 204],
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[429, 210],
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[429 ,216],
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[430, 222],
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[431, 227],
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[431, 233],
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[431, 239],
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[432, 245],
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[433, 250],
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[434, 256],
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[435 ,261],
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[436 ,267],
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[437 ,272],
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[438, 278],
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[439, 283],
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[441, 289],
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[442, 294],
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[443, 300],
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[445, 305],
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[446, 310],
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[448, 316],
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[450, 321],
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[453, 330],
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[461, 334],
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[466, 336],
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[471, 338],
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[477 ,340],
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[482, 340],
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[488, 341],
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[494, 341],
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[500, 341],
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[506 ,341],
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[511 ,340],
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[517, 339],
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[523 ,338],
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[528, 337],
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[533, 335],
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[539, 333],
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[544, 331],
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[549, 329],
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[553, 326],
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[558, 322],
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[562, 318],
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[566, 313],
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[568, 308],
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[569, 303],
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[570, 297],
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[570, 291],
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[569, 285],
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[569, 280],
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[568, 274],
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[567, 268],
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[566 ,263],
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[566 ,257],
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[564 ,252],
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[564 ,246],
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[562, 241],
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[561, 235],
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[560, 230],
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[560, 224],
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[558 ,219],
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[558, 213],
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[556, 207],
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[555, 202],
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[554, 196],
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[553, 191],
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[552, 185],
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[550, 180],
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[549, 174],
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[548, 169],
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[547 ,164],
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[545 ,158],
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[543 ,153],
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[541 ,148],
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[539 ,143],
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[536 ,138],
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[533 ,133],
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[530, 128],
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[528 ,124],
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[524 ,119],
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[520 ,115],
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[515, 111],
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[511, 108],
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[506 ,106],
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[501 ,104],
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[495 ,102],
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[490, 101],
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[485 , 99],
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[480 , 97],
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[475 , 93],
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[471 , 89],
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[468 , 84],
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[465, 80],
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[460 , 76]]
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data1 = scale(data,-100)
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print(data1)
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temp = np.ones((1300,1000,3), np.uint8) * 255
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#cv2.polylines(temp, data1, 1, 255)
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cv2.polylines(temp , [np.array(data , dtype=np.int32)], True, (255, 0, 0), 1)
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cv2.polylines(temp , [np.array(data1 , dtype=np.int32)], True, (0, 0, 255), 1)
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cv2.imshow ("img",temp)
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cv2.imwrite("1.jpg",temp)
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cv2.waitKey(0)
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