179 lines
6.2 KiB
Python
179 lines
6.2 KiB
Python
import cv2
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print(cv2.__version__) # 4.9.0
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import json
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import math
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import copy
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import numpy as np
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def get_params(num_param, start, end):
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return copy.deepcopy(num_param[start:end+1])
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def pramas_to_num(text_param):
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for item in text_param:
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item['center'] = float(item['center']) * 1000
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item['x'] = int(item['x'])
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item['w'] = int(item['w'])
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item['h'] = int(item['h'])
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return text_param
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def pramas_to_text(num_param):
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for item in num_param:
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item['center'] = str(item['center'])
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item['x'] = str(item['x'])
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item['w'] = str(item['w'])
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item['h'] = str(item['h'])
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item['x1'] = str(item['x1'])
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item['y1'] = str(item['y1'])
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item['x2'] = str(item['x2'])
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item['y2'] = str(item['y2'])
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item['x3'] = str(item['x3'])
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item['y3'] = str(item['y3'])
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item['x4'] = str(item['x4'])
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item['y4'] = str(item['y4'])
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return num_param
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def sort_params(params):
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sorted_params = sorted(params, key=lambda item: (item['center'], item['x']))
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return sorted_params
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def print_params(sort_params):
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for param in sort_params:
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print(param["center"], param["x"], param["w"], param["h"])
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def print_path(search_path):
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for path in search_path:
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print(path[0], path[1])
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def search_path(sort_params):
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searchPath = []
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for i in range(len(sort_params) - 1):
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(r, theta) = cartesian_to_polar(sort_params[i]["x"], sort_params[i]["center"],
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sort_params[i + 1]["x"], sort_params[i + 1]["center"])
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searchPath.append([r, theta])
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return searchPath
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def normalize_params_and_path(sort_params, search_path, index=0):
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base = sort_params[index]["h"]
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for param in sort_params:
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param['center'] /= base
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param['x'] /= base
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param['w'] /= base
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param['h'] /= base
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param['w/h'] = param['w'] / param['h']
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if search_path != None:
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for path in search_path:
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path[0] /= base
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# path[1] /= base
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return sort_params, search_path
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def read_from_json(file_path):
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with open(file_path, 'r') as f:
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loaded_array = json.load(f)
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return loaded_array
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def cartesian_to_polar(x1, y1, x2, y2):
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dx = x2 - x1
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dy = y2 - y1
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r = math.sqrt(dx**2 + dy**2)
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theta = math.atan2(dy, dx)
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return r, theta
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def calculate_second_point(x1, y1, r, theta_radians):
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# theta_radians = math.radians(theta_degrees)
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x2 = x1 + r * math.cos(theta_radians)
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y2 = y1 + r * math.sin(theta_radians)
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return x2, y2
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def filter_params(data_bim_candi, search_path, sort_params):
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loop = 0
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for path in search_path:
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r = path[0]
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theta = path[1]
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cur_parmas = sort_params[loop]
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next_params = sort_params[loop + 1]
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exclude_list = []
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for i in range(len(data_bim_candi)):
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param = data_bim_candi[i]["params"][data_bim_candi[i]["index"]]
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center = param["center"]
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x = param["x"]
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h = param["h"]
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w = param["w"]
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hmw = param["w/h"]
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# 长宽比大于1.5或小于0.67，则配出当前选项
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if hmw / cur_parmas["w/h"] > 1.5 or hmw / cur_parmas["w/h"] < 0.67:
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exclude_list.append(data_bim_candi[i])
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break
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# 判断下一跳位置是否击中目标
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tmp_x, tmp_y = calculate_second_point(x, center, r, theta)
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find = False
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for j in range(len(data_bim_candi)):
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tmp_param = data_bim_candi[i]["params"][j]
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tmp_x = tmp_param["x"]
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tmp_y = tmp_param["center"]
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tmp_distence = np.linalg.norm(np.asarray([tmp_x, tmp_y]) - np.asarray([x, center]))
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if abs(tmp_distence - r) < r / 10:
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data_bim_candi[i]["index"] =
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break
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if not find:
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exclude_list.append(data_bim_candi[i])
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print(data_bim_candi[i]["params"][i])
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loop += 1
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if __name__ == "__main__":
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# im_bim = cv2.imread("")
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# im_sub = cv2.imread("")
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data_bim = {}
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data_bim["type"] = 0
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data_bim["params"] = read_from_json("./params.json")
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data_bim["point"] = []
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data_sub_0_4 = {}
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data_sub_0_4["type"] = 0
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data_sub_0_4["params"] = get_params(data_bim["params"], 0, 4)
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data_sub_0_4["point"] = []
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data_sub_4_9 = {}
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data_sub_4_9["type"] = 0
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data_sub_4_9["params"] = get_params(data_bim["params"], 4, 9)
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data_sub_4_9["point"] = []
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data_sub_10_14 = {}
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data_sub_10_14["type"] = 0
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data_sub_10_14["params"] = get_params(data_bim["params"], 10, 14)
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data_sub_10_14["point"] = []
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print(data_sub_10_14["params"])
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##################### 开始计算 ####################
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# 1、计算sub的搜索路径
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## 1.1 排序 y升序，x升序
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data_bim["params"] = pramas_to_num(data_bim["params"])
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data_sub = data_sub_0_4
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data_sub["params"] = pramas_to_num(data_sub["params"])
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data_sub["params"] = sort_params(data_sub["params"])
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_sort_params = data_sub["params"]
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## 1.2生成路径数据
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_search_path = search_path(_sort_params)
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# print(_search_path)
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## 1.3 归一化 sort_params 和 searchPath
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_sort_params, _search_path = normalize_params_and_path(_sort_params, _search_path)
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print_params(_sort_params)
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print_path(_search_path)
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# 2、开始n轮bim图的搜索，首轮全部数据参与计算，每轮淘汰若干选项，直到整个路径搜索完毕，剩下的选项都是候选目标区域
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## 2.1 给bim所有的预埋件都分配一个完整的图
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num = len(data_bim["params"])
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_data_bim_candi = []
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for i in range(num):
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data_bim_copy = {}
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data_bim_copy["params"] = get_params(data_bim["params"], 0, num-1)
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data_bim_copy["index"] = i
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_sort_params, _ = normalize_params_and_path(data_bim_copy["params"], None, i) # 用第i个件作为归一化指标
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_data_bim_candi.append(data_bim_copy)
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## 2.2 判断 data_sub["params"] w 和 h 的比值
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filter_params(_data_bim_candi, _search_path, _sort_params) |