732 lines
30 KiB
Python
732 lines
30 KiB
Python
import cv2
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from utils import filter_rectangle, get_hd_cam_rect, get_hd_roi_from_txt
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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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import time
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from scipy.spatial import distance
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from scipy.optimize import linear_sum_assignment
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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 parmas_to_num(text_param):
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for item in text_param:
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# item['center'] = int(float(item['center']) * 1000)
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item['center'] = int(float(item['center']))
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item['x'] = int(float(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 parmas_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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item['x_center'] = str(item['x_center'])
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item['y_center'] = str(item['y_center'])
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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 cal_c1c2c3c4(param, heigt):
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'''
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按照上左、上右、下右、下左的顺时针顺序.
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返回的数据是转换成了以左上角为原点的坐标系下的坐标点。
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'''
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param['x1'] = int(param['x'] - param['w'] / 2)
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param['y1'] = heigt - int(param['center'] + param['h'] / 2)
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param['x2'] = int(param['x'] + param['w'] / 2)
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param['y2'] = heigt - int(param['center'] + param['h'] / 2)
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param['x3'] = int(param['x'] + param['w'] / 2)
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param['y3'] = heigt - int(param['center'] - param['h'] / 2)
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param['x4'] = int(param['x'] - param['w'] / 2)
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param['y4'] = heigt - int(param['center'] - param['h'] / 2)
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param['x_center'] = int((param['x1'] + param['x2']) / 2)
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param['y_center'] = int((param['y1'] + param['y3']) / 2)
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return param
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def gen_im_from_params(params, type="lines"):
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# 依据bim数据生成bim图
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# type: # line points
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## 确定整个bim图的长度和宽度边界
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max_y = -999999 # y坐标最大值
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max_y_idx = -1 # y坐标最大值的索引
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max_x = -999999 # x坐标最大值
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max_x_idx = -1 # x坐标最大值的索引
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# 遍历,找到矩形x坐标最大值和矩形y坐标最大值
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for i, param in enumerate(params):
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# x中心点坐标加上宽度的一半 = 当前矩形的x最大坐标
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if param["x"] + param["w"] / 2 > max_x:
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# 如果是最大的x坐标就更新
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max_x = param["x"] + param["w"] / 2
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max_x_idx = i
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# y中心点坐标加上高度的一半 = 当前矩形的y最大坐标
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if param["center"] + param["h"] / 2 > max_y:
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# 如果是最大的y坐标就更新
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max_y = param["center"] + param["h"] / 2
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max_y_idx = i
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padding_value = 1000 # 内边距,避免整个bim图片贴着边缘展示
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bim_width = int(max_x) + padding_value
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print(f"[bim_width] ====== [{bim_width}]")
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bim_height = int(max_y) + padding_value
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print(f"[bim_height] ====== [{bim_height}]")
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bim_channels = 3
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im = np.zeros((bim_height, bim_width, bim_channels), dtype=np.uint8)
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for i, param in enumerate(params):
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cal_c1c2c3c4(param, bim_height)
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if type == "lines":
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pts = np.asarray([[param['x1'], param['y1']],
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[param['x2'], param['y2']],
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[param['x3'], param['y3']],
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[param['x4'], param['y4']]])
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cv2.polylines(im, [pts], True, (255, 255, 0), 8)
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elif type == "points":
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cv2.circle(im, (param['x1'], param['y1']), 1, 255, 20)
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cv2.circle(im, (param['x2'], param['y2']), 1, 255, 20)
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cv2.circle(im, (param['x3'], param['y3']), 1, 255, 20)
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cv2.circle(im, (param['x4'], param['y4']), 1, 255, 20)
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cv2.circle(im, (param['x'], int((param['y3'] + param['y2']) / 2)), 1, (255, 0, 0), 8)
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return im
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"""
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判断点是否在区域内部
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True 在
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False 不在
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如果没有提供区域,不做判断,返回True
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"""
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def is_inside_roi(point, roi_w, roi_h):
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if (roi_w != None and roi_h != None):
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x = point[0]
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y = point[1]
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if (x <= 0 or y <= 0 or y >= roi_h or x >= roi_w):
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# 不在区域内部
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return False
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else:
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# 在区域内部
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return True
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else:
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# 没有提供区域,不做判断,默认返回True
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return True
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def gen_points_from_params(params, roi_w=None, roi_h=None):
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# 依据bim数据生成bim图
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# type line points
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## 计算bim图长和宽
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max_y = -999999
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max_y_idx = -1
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max_x = -999999
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max_x_idx = -1
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for i, param in enumerate(params):
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if param["x"] + param["w"] / 2 > max_x:
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max_x = param["x"] + param["w"] / 2
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max_x_idx = i
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if param["center"] + param["h"] / 2 > max_y:
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max_y = param["center"] + param["h"] / 2
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max_y_idx = i
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bim_height = int(max_y)
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points = []
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for i, param in enumerate(params):
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# 排序点 按照上左、上右、下右、下左的顺时针顺序
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if (roi_w == None and roi_h == None):
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cal_c1c2c3c4(param, bim_height)
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# 过滤点,把在roi区域之外的点全部过滤掉.
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if (is_inside_roi([param['x1'], param['y1']], roi_w, roi_h)):
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points.append([param['x1'], param['y1'], i])
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if (is_inside_roi([param['x2'], param['y2']], roi_w, roi_h)):
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points.append([param['x2'], param['y2'], i])
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if (is_inside_roi([param['x3'], param['y3']], roi_w, roi_h)):
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points.append([param['x3'], param['y3'], i])
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if (is_inside_roi([param['x4'], param['y4']], roi_w, roi_h)):
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points.append([param['x4'], param['y4'], i])
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if (is_inside_roi([param['x_center'], param['y_center']], roi_w, roi_h)):
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points.append([param['x_center'], param['y_center'], i])
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if (roi_w != None and roi_h != None):
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print(f"[经区域过滤之后的点数一共为] ====== [{len(points)}]")
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return points
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def topological_similarity(adj1, adj2):
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"""
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计算两个拓扑结构的相似度
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"""
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# 计算邻接矩阵的相似度
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similarity = np.sum(adj1 == adj2) / (adj1.shape[0] * adj2.shape[1])
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return similarity
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def find_topological_matches(points1, adj1, points2, adj2, threshold=0.8):
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"""
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基于拓扑结构寻找匹配点集
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"""
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matches = []
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for i in range(len(points1)):
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for j in range(len(points2)):
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# 计算拓扑相似度
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sim = topological_similarity(adj1[i], adj2[j])
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if sim > threshold:
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matches.append((i, j, sim))
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# 按相似度排序
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matches.sort(key=lambda x: x[2], reverse=True)
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return matches
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from sklearn.linear_model import RANSACRegressor
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def ransac_shape_matching(points, reference_points):
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model_ransac = RANSACRegressor(random_state=42)
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try:
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model_ransac.fit(reference_points, points[:len(reference_points)]) # 假设点数相同
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except ValueError as e:
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print("Error fitting the model:", e)
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return []
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inlier_mask = model_ransac.inlier_mask_
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best_match_subset = points[inlier_mask]
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return best_match_subset
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def polar_to_cartesian(polar_points):
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r = polar_points[:, 0]
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theta = polar_points[:, 1]
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x = r * np.cos(theta)
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y = r * np.sin(theta)
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return np.vstack((x, y)).T
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def compute_shape_context(points, nbins_r=5, nbins_theta=12):
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n = points.shape[0]
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shape_contexts = []
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r_max = np.max(distance.pdist(points))
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r_edges = np.logspace(-1, np.log10(r_max), nbins_r)
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theta_edges = np.linspace(-np.pi, np.pi, nbins_theta + 1)
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for i in range(n):
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current_point = points[i]
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relative_points = points - current_point
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rs = np.hypot(relative_points[:, 0], relative_points[:, 1])
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thetas = np.arctan2(relative_points[:, 1], relative_points[:, 0])
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H, _, _ = np.histogram2d(thetas, rs, bins=[theta_edges, r_edges])
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H /= np.sum(H) + 1e-8 # 归一化
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shape_contexts.append(H.flatten())
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return np.array(shape_contexts)
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def match_shapes(sc1, sc2):
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cost_matrix = distance.cdist(sc1, sc2, metric='sqeuclidean')
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row_ind, col_ind = linear_sum_assignment(cost_matrix)
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return cost_matrix[row_ind, col_ind].sum()
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def _sobel(image):
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'''
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_sobel
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'''
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if image.ndim > 2:
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image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# todo 增加几个参数 http://blog.csdn.net/sunny2038/article/details/9170013
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_sobelx = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=1)
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_sobely = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=1)
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_sobelx = np.uint8(np.absolute(_sobelx))
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_sobely = np.uint8(np.absolute(_sobely))
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_sobelcombine = cv2.bitwise_or(_sobelx, _sobely)
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return _sobelcombine
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def _findContours(image):
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'''
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_findContours
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http://blog.csdn.net/mokeding/article/details/20153325
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'''
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contours, _ = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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return sorted(contours, key=cv2.contourArea, reverse=True)
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def bim_compare_to_hd_roi(hd_roi_list,hd_img_width,hd_img_height,hd_cam_w_bim,hd_cam_h_bim):
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"""
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预埋件号码匹配
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将高清摄像头照片的ROI和实际BIM图中的号码匹配上
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参数:
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hd_roi_list : 高清摄像头的ROI列表，每个ROI表示一个可能的预埋件区域
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1.每个第二层子数组中的坐标顺序为左上、右上、右下、左下（顺时针）
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2.是以左上角为原点的常规的图片坐标系。
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格式如下。
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[
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[[x1, y1], [x2, y2], [x3, y3], [x4, y4]],
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[[x1, y1], [x2, y2], [x3, y3], [x4, y4]],
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]
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hd_img_width (int): 高清相机拍摄图片原始宽度
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hd_img_height (int): 高清相机拍摄图片原始高度
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hd_cam_w_bim (int): 高清相机矩形示意框在bim图中的宽度
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hd_cam_h_bim (int): 高清相机矩形示意框在bim图中的高度
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返回:
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"""
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if __name__ == "__main__":
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# 读取并处理数据
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data_bim = {}
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data_bim["type"] = 0
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data_bim["params"] = read_from_json("data_bim.json")
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data_bim["point"] = []
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data_bim["params"] = parmas_to_num(data_bim["params"])
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data_sub = {}
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data_sub["type"] = 0
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data_sub["params"] = []
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# 广角相机拍照照片之后左右边界的裁剪比例。
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wide_cam_left_cut_rate = 0.1
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wide_cam_right_cut_rate = 0.1
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# 高清相机的视场矩形在广角相机里面的坐标。cv2下的图片坐标系，以左上角为坐标原点
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hd_cam_x, hd_cam_y, hd_cam_w, hd_cam_h, = get_hd_cam_rect(wide_cam_left_cut_rate)
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# 创建测试子集
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# sub_im = cv2.imread("wide_image.png")
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# sub_zero = np.zeros_like(sub_im)
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# _im_gray = cv2.cvtColor(sub_im, cv2.COLOR_BGR2GRAY)
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# _im_gray = cv2.GaussianBlur(_im_gray, (5, 5), 0)
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# _im_edge_sobel = _sobel(_im_gray)
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# _, _im_thresh = cv2.threshold(_im_edge_sobel, 5, 255, cv2.THRESH_BINARY)
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# cnts = _findContours(_im_thresh)
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# for contour in cnts:
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# x, y, w, h = cv2.boundingRect(contour)
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# cv2.rectangle(sub_zero, (x, y), (x + w, y + h), (255, 255, 255), -1)
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# _im_edge_sobel = _sobel(sub_zero)
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# _, _im_thresh = cv2.threshold(_im_edge_sobel, 5, 255, cv2.THRESH_BINARY)
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# cnts = _findContours(_im_thresh)
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original_rectangle = read_from_json("data_sub/test_1/data_sub.json")
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# 过滤矩形
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# cnts 过滤之后的矩形
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# sub_im 裁剪之后的图像
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cnts, sub_im = filter_rectangle("data_sub/test_1/wide_image.png", original_rectangle, wide_cam_left_cut_rate,
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wide_cam_right_cut_rate)
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sub_zero = np.zeros_like(sub_im)
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for contour in cnts:
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# x, y, w, h = cv2.boundingRect(contour)
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x = contour["x"]
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y = contour["y"]
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w = contour["width"]
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h = contour["height"]
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# 由于定位框大小大于预埋件大小，因此这里需要做缩放处理
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kh = int(h * 0.01) # roi1
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kw = int(w * 0.01) # roi1
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x += int(kw)
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y += int(kh)
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w -= int(kw)
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h -= int(kh)
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param = {}
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param["x1"] = x
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param["y1"] = y
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param["x2"] = x + w
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param["y2"] = y
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param["x3"] = x + w
|
||
param["y3"] = y + h
|
||
param["x4"] = x
|
||
param["y4"] = y + h
|
||
param['x_center'] = int((param['x1'] + param['x2']) / 2)
|
||
param['y_center'] = int((param['y1'] + param['y3']) / 2)
|
||
param["w"] = param["x2"] - param["x1"]
|
||
param["h"] = param["y3"] - param["y1"]
|
||
param["x"] = int((param["x1"] + param["x2"]) / 2)
|
||
param['center'] = sub_im.shape[0] - int((param["y1"] + param["y3"]) / 2)
|
||
data_sub["params"].append(param)
|
||
cv2.rectangle(sub_zero, (x, y), (x + w, y + h), (0, 255, 0), 1)
|
||
# bim_im = gen_im_from_params(data_bim["params"])
|
||
# cv2.namedWindow("bim", cv2.WINDOW_NORMAL)
|
||
# cv2.imshow("bim", bim_im)
|
||
# cv2.imwrite("bim_im.png", bim_im)
|
||
# cv2.waitKey(0)
|
||
cv2.imshow("sub_zero", sub_zero)
|
||
# cv2.waitKey(0)
|
||
|
||
# data_sub = {}
|
||
# data_sub["type"] = 0
|
||
# data_sub["params"] = get_params(data_bim["params"], 1,8)
|
||
# data_sub["point"] = []
|
||
# data_sub["params"][0]["center"] += 20
|
||
# data_sub["params"][0]["x"] += 13
|
||
# data_sub["params"][0]["w"] += 40
|
||
|
||
##################### 开始计算 ####################
|
||
# 1、计算sub的搜索路径
|
||
## 1.1 排序 y升序，x升序
|
||
data_sub["params"] = sort_params(data_sub["params"])
|
||
_sub_sort_params = data_sub["params"]
|
||
start_time = time.time()
|
||
|
||
# sub_roi_height = int(max_y) #????
|
||
# sub_roi_width = int(max_x) #????
|
||
sub_roi_height = sub_im.shape[0]
|
||
sub_roi_width = sub_im.shape[1]
|
||
sub_roi_params_select_id = -1
|
||
sub_roi_w_base_len = 0 # 选中预埋件的宽度作为基础长度
|
||
sub_roi_divide_w_h = 1
|
||
polar_origin_x = 0 # 极坐标原点 x
|
||
polar_origin_y = 0 # 极坐标原点 y
|
||
start_x = 0
|
||
start_y = 0
|
||
## 1.2 选择一块完整的预埋件
|
||
for i, param in enumerate(_sub_sort_params):
|
||
if 0 < param['x1'] and param['x2'] < sub_roi_width \
|
||
and 0 < param['y1'] and param['y3'] < sub_roi_height:
|
||
sub_roi_params_select_id = i
|
||
sub_roi_w_base_len = param['x2'] - param['x1']
|
||
sub_roi_divide_w_h = param['w'] / param['h']
|
||
polar_origin_x = int(param['x1']) # 当前选择的预埋件的左上角 x 坐标
|
||
polar_origin_y = int(param['y1']) # 当前选择的预埋件的左上角 y 坐标
|
||
break
|
||
if sub_roi_params_select_id == -1 or sub_roi_w_base_len == 0:
|
||
print("[ERROR]\t 拍摄的图像中没有完整的预埋件信息\n")
|
||
assert (0)
|
||
## 1.2.2 将其他预埋件相对于它的极坐标进行填写
|
||
for i, param in enumerate(_sub_sort_params):
|
||
if i != sub_roi_params_select_id:
|
||
param['r'], param['theta'] = cartesian_to_polar(_sub_sort_params[sub_roi_params_select_id]['x_center'],
|
||
_sub_sort_params[sub_roi_params_select_id]['y_center'],
|
||
param['x_center'], param['y_center'])
|
||
|
||
## 1.3计算所有点到该预埋件左上点的，点个数，平均极半径 和 平均极角度
|
||
sum_r, sum_theta = 0.0, 0
|
||
count = 0
|
||
# 测试,画出所有的pts
|
||
# for i, p in enumerate(_sub_sort_params):
|
||
# # 画点
|
||
# cv2.circle(sub_im, (p["x_center"], p["y_center"]), 2, (0, 255, 255), -1)
|
||
# cv2.imshow("_sub_sort_params", sub_im)
|
||
# cv2.waitKey(0)
|
||
pts = gen_points_from_params(_sub_sort_params, sub_roi_width, sub_roi_height)
|
||
# # 测试,画出所有的pts
|
||
for i, p in enumerate(pts):
|
||
# 画点
|
||
cv2.circle(sub_im, (p[0], p[1]), 2, (0, 255, 255), -1)
|
||
# 写编号
|
||
cv2.putText(sub_im, str(i), (p[0], p[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
|
||
cv2.rectangle(sub_im, (hd_cam_x, hd_cam_y), (hd_cam_x + hd_cam_w, hd_cam_y + hd_cam_h), (0, 255, 0), 4)
|
||
cv2.imshow("sub_im_points", sub_im)
|
||
# cv2.waitKey(0)
|
||
polar_list = []
|
||
for i, pt in enumerate(pts):
|
||
r, theta = cartesian_to_polar(polar_origin_x, polar_origin_y, pt[0], pt[1])
|
||
sum_r += r
|
||
sum_theta += theta
|
||
count += 1
|
||
polar_list.append([r, theta])
|
||
sum_r /= count * sub_roi_w_base_len
|
||
sum_theta /= count
|
||
print(f"[所有点到该预埋件左上点的个数] ====== [{count}]")
|
||
print(f"[所有点到该预埋件左上点的平均极半径] ====== [{sum_r}]")
|
||
print(f"[所有点到该预埋件左上点的平均极角] ====== [{sum_theta}]")
|
||
|
||
# 初始化候选预埋件
|
||
candi_params = []
|
||
for i, param in enumerate(data_bim["params"]):
|
||
temp_div_w_h = param['w'] / param['h']
|
||
if temp_div_w_h / sub_roi_divide_w_h < 1.5 and temp_div_w_h / sub_roi_divide_w_h > 0.66:
|
||
candi_params.append(param)
|
||
print(f"形状筛选后还剩下：{len(candi_params)} 个候选, w / h = {sub_roi_divide_w_h}")
|
||
|
||
rst_params = []
|
||
bim_all_pts = gen_points_from_params(data_bim["params"])
|
||
bim_im = gen_im_from_params(data_bim["params"])
|
||
# sub_im = gen_im_from_params(data_sub["params"])# 需要读取
|
||
min_match_score = 999999
|
||
for i, param in enumerate(candi_params):
|
||
tmp_roi_w_base_len = param['x2'] - param['x1']
|
||
scale = tmp_roi_w_base_len / sub_roi_w_base_len
|
||
tmp_roi_width = int(scale * sub_roi_width)
|
||
tmp_roi_height = int(scale * sub_roi_height)
|
||
# 相对于bim图的坐标
|
||
tmp_roi_start_x = int(param['x1'] - scale * polar_origin_x)
|
||
tmp_roi_end_x = tmp_roi_start_x + tmp_roi_width
|
||
|
||
tmp_roi_start_y = int(param['y1'] - scale * polar_origin_y)
|
||
tmp_roi_end_y = tmp_roi_start_y + tmp_roi_height
|
||
|
||
# 计算高清矩形，在bim上面的坐标。相当于被scale一起放大了
|
||
hd_cam_x_bim = int(scale * hd_cam_x + tmp_roi_start_x)
|
||
hd_cam_y_bim = int(scale * hd_cam_y + tmp_roi_start_y)
|
||
hd_cam_w_bim = int(scale * hd_cam_w)
|
||
hd_cam_h_bim = int(scale * hd_cam_h)
|
||
|
||
tmp_roi_conners = [[tmp_roi_start_x, tmp_roi_start_y],
|
||
[tmp_roi_end_x, tmp_roi_start_y],
|
||
[tmp_roi_end_x, tmp_roi_end_y],
|
||
[tmp_roi_start_x, tmp_roi_end_y]]
|
||
tmp_sum_r, tmp_sum_theta = 0.0, 0
|
||
tmp_count = 0
|
||
tmp_polar_list = []
|
||
# 这里需要把选中的预埋件也提取出来
|
||
param['effective_points'] = []
|
||
for j, pt in enumerate(bim_all_pts):
|
||
if cv2.pointPolygonTest(np.asarray(tmp_roi_conners), (pt[0], pt[1]), False) > 0:
|
||
r, theta = cartesian_to_polar(param['x1'], param['y1'], pt[0], pt[1])
|
||
tmp_sum_r += r
|
||
tmp_sum_theta += theta
|
||
tmp_count += 1
|
||
tmp_polar_list.append([r, theta])
|
||
# 搜集有效点，以供后续继续处理
|
||
param['effective_points'].append(pt)
|
||
tmp_sum_r /= tmp_count * tmp_roi_w_base_len
|
||
tmp_sum_theta /= tmp_count
|
||
|
||
# 预埋件数量相差 30%，则不进行计算
|
||
# if tmp_count / count > 1.3 or tmp_count / count < 0.77: continue
|
||
|
||
if abs(tmp_count - count) == 0:
|
||
score = 0.5
|
||
elif abs(tmp_count - count) <= 10:
|
||
score = 0.4
|
||
elif abs(tmp_count - count) <= 20:
|
||
score = 0.3
|
||
elif abs(tmp_count - count) <= 30:
|
||
score = 0.2
|
||
else:
|
||
score = 0.0
|
||
|
||
# else: score = (1 / abs(tmp_count - count) ) * 0.7
|
||
score += (1 - abs(tmp_sum_r - sum_r) / sub_roi_width) * 0.25
|
||
score += (1 - abs(tmp_sum_theta - sum_theta) / 3.14) * 0.35
|
||
|
||
print("score=======", str(score))
|
||
if score > 0.6: #????
|
||
cartesian_points1 = polar_to_cartesian(np.asarray(tmp_polar_list)) # bim上的坐标
|
||
cartesian_points2 = polar_to_cartesian(np.asarray(polar_list)) # sub上的坐标
|
||
sc1 = compute_shape_context(cartesian_points1)
|
||
sc2 = compute_shape_context(cartesian_points2)
|
||
match_score = match_shapes(sc1, sc2)
|
||
print("score>0.6")
|
||
print(f"[score] ====== [{score}]")
|
||
print(f"[match_score] ====== [{match_score}]")
|
||
print(f"[tmp_count] ====== [{tmp_count}]")
|
||
print(f"[tmp_sum_r] ====== [{tmp_sum_r}]")
|
||
print(f"[tmp_sum_theta] ====== [{tmp_sum_theta}]")
|
||
if match_score < 5.0: #????
|
||
param["start_point"] = (tmp_roi_start_x, tmp_roi_start_y)
|
||
param["end_point"] = (tmp_roi_end_x, tmp_roi_end_y)
|
||
param["score"] = (score, tmp_count, tmp_sum_r * tmp_roi_w_base_len, tmp_sum_theta)
|
||
param['match_score'] = match_score
|
||
# 高清相机的矩形数据
|
||
param['hd_cam_x_bim'] = hd_cam_x_bim
|
||
param['hd_cam_y_bim'] = hd_cam_y_bim
|
||
param['hd_cam_w_bim'] = hd_cam_w_bim
|
||
param['hd_cam_h_bim'] = hd_cam_h_bim
|
||
if min_match_score > match_score:
|
||
min_match_score = match_score
|
||
print(f"[start_point] ====== [{param["start_point"]}]")
|
||
print(f"[end_point] ====== [{param["end_point"]}]")
|
||
rst_params.append(param)
|
||
|
||
# 找到得分最大的
|
||
max_score = -99999
|
||
max_index = -1
|
||
for i, param in enumerate(rst_params):
|
||
score = param["score"]
|
||
match_score = param["match_score"]
|
||
if abs(score[0] / match_score) > max_score:
|
||
max_score = abs(score[0] / match_score)
|
||
max_index = i
|
||
rst_p = rst_params[max_index]
|
||
bim_im = cv2.rectangle(bim_im, rst_p["start_point"], rst_p["end_point"], 100 * (i + 1), 50)
|
||
# 画出高清相机矩形框
|
||
bim_im = cv2.rectangle(bim_im, (rst_p["hd_cam_x_bim"], rst_p["hd_cam_y_bim"]), (
|
||
rst_p["hd_cam_x_bim"] + rst_p["hd_cam_w_bim"], rst_p["hd_cam_y_bim"] + rst_p["hd_cam_h_bim"],), (0, 0, 255), 40)
|
||
|
||
# ====================== 预埋件号码匹配 开始 =========================
|
||
hd_roi_list = get_hd_roi_from_txt("data_sub/test_1/roi_conners.txt") # 高清相机识别的ROI坐标
|
||
# 高清相机坐标转换为bim图坐标
|
||
hd_img_width = 9144 # 高清相机的图片宽度
|
||
hd_img_height = 7000 # 高清相机的图片高度
|
||
# 宽高肯定都是等比的直接按照bim上面高清矩形的宽度，算下比例。
|
||
scale_w = rst_p["hd_cam_w_bim"] / hd_img_width
|
||
scale_h = rst_p["hd_cam_h_bim"] / hd_img_height
|
||
hd_roi_out_of_range_index = [] # 超出边界的ROI的索引
|
||
for hd_roi_index, hd_roi in enumerate(hd_roi_list):
|
||
for i in range(4):
|
||
# 所有高清照片的ROI先按照这个比例转换 ，并且加上坐标偏移，转换为bim上实际坐标
|
||
hd_roi[i][0] = int(hd_roi[i][0] * scale_w) + rst_p["hd_cam_x_bim"]
|
||
hd_roi[i][1] = int(hd_roi[i][1] * scale_h) + rst_p["hd_cam_y_bim"]
|
||
# 如果ROI坐标形成的矩形，横跨bim图上高清矩形范围边界框，就剔除这个矩形
|
||
is_x_cross_left_border_line = hd_roi[0][0] < rst_p["hd_cam_x_bim"] <= hd_roi[1][0]
|
||
is_x_cross_right_border_line = hd_roi[0][0] < rst_p["hd_cam_x_bim"] + rst_p["hd_cam_w_bim"] <= hd_roi[1][0]
|
||
is_y_cross_top_border_line = hd_roi[0][1] < rst_p["hd_cam_y_bim"] <= hd_roi[2][1]
|
||
is_y_cross_bottom_border_line = hd_roi[0][1] < rst_p["hd_cam_y_bim"] + rst_p["hd_cam_h_bim"] <= hd_roi[2][1]
|
||
if is_x_cross_left_border_line or is_x_cross_right_border_line or is_y_cross_top_border_line or is_y_cross_bottom_border_line:
|
||
hd_roi_out_of_range_index.append(hd_roi_index)
|
||
# 画出来试试
|
||
# bim_im = cv2.rectangle(bim_im, (hd_roi[0][0], hd_roi[0][1]), (hd_roi[2][0], hd_roi[2][1]), (0, 0, 255), 30)
|
||
# 写上i编号
|
||
# cv2.putText(bim_im, str(hd_roi_index), (hd_roi[0][0], hd_roi[0][1]), cv2.FONT_HERSHEY_SIMPLEX, 5, (0, 0, 255),20)
|
||
print(f"超出边界的ROI的索引是:{hd_roi_out_of_range_index}")
|
||
|
||
# bim所有的矩形，注意bim原数据是以左下角为原点的坐标系 ！！！
|
||
# bim所有的矩形，注意bim原数据是以左下角为原点的坐标系 ！！！
|
||
# bim所有的矩形，注意bim原数据是以左下角为原点的坐标系 ！！！
|
||
bim_rect_list = data_bim["params"]
|
||
# 遍历bim所有的矩形，找到与高清相机ROI匹配的矩形
|
||
bim_rect_hit_list = []
|
||
for bim_rect_index, bim_rect_item in enumerate(bim_rect_list):
|
||
# x不需要转换
|
||
bim_rect_item_center_x = bim_rect_item["x"]
|
||
# 把y坐标系转换成左上角坐标系
|
||
bim_im_height = bim_im.shape[0]
|
||
bim_rect_item_center_y = bim_im_height - bim_rect_item["center"]
|
||
# 逐一和高清相机ROI坐标进行匹配
|
||
for hd_roi_index, hd_roi in enumerate(hd_roi_list):
|
||
# 如果中心点的坐标在某个高清相机ROI内，就认为匹配上了。
|
||
bim_rect_item_center_x_inside = hd_roi[0][0] < bim_rect_item_center_x < hd_roi[1][0]
|
||
bim_rect_item_center_y_inside = hd_roi[0][1] < bim_rect_item_center_y < hd_roi[2][1]
|
||
if bim_rect_item_center_x_inside and bim_rect_item_center_y_inside:
|
||
bim_rect_hit_list.append({
|
||
"bim_rect": bim_rect_item,
|
||
"hd_roi": hd_roi
|
||
})
|
||
# 画出bim_rect_hit_list
|
||
for bim_rect_hit_item in bim_rect_hit_list:
|
||
bim_rect = bim_rect_hit_item["bim_rect"]
|
||
hd_roi = bim_rect_hit_item["hd_roi"]
|
||
bim_im = cv2.rectangle(bim_im, (hd_roi[0][0], hd_roi[0][1]), (hd_roi[2][0], hd_roi[2][1]), (0, 0, 255), 30)
|
||
# 写上i编号
|
||
cv2.putText(bim_im, str(bim_rect["code"]), (hd_roi[0][0]+100, hd_roi[0][1]+200), cv2.FONT_HERSHEY_SIMPLEX, 5, (0, 0, 255),
|
||
20)
|
||
|
||
# ===================== 预埋件号码匹配 结束 ===========================
|
||
|
||
# # 预埋件匹配
|
||
# for i, param in enumerate(rst_params):
|
||
# score = param["score"]
|
||
# match_score = param["match_score"]
|
||
# print(f"[match_score] ====== [{match_score}]")
|
||
#
|
||
# id = param["code"]
|
||
#
|
||
# if min_match_score == match_score or match_score < 5.0: #????
|
||
# # if True:
|
||
# index_list = []
|
||
# for j in range(len(param['effective_points'])):
|
||
# pt = param['effective_points'][j]
|
||
# if pt[2] not in index_list:
|
||
# index_list.append(param['effective_points'][j][2])
|
||
# # for i, param in enumerate(_sub_sort_params):
|
||
# # param[]
|
||
# print(f"起始预埋件ID：{id}，置信度为：{score[0]}，点数量：{score[1]}，平均长度为：{score[2]}，平均角度为：{score[3]}")
|
||
# print(f"match_sscore为：{match_score}")
|
||
# print(f"[start_point] ====== [{param["start_point"]}]")
|
||
# print(f"[end_point] ====== [{param["end_point"]}]")
|
||
#
|
||
# bim_im = cv2.rectangle(bim_im, param["start_point"], param["end_point"], 100 * (i + 1), 50)
|
||
|
||
elapsed_time = time.time() - start_time
|
||
print(f"Execution time: {elapsed_time:.4f} seconds")
|
||
img_matches = cv2.resize(bim_im, (int(bim_im.shape[1] / 6), int(bim_im.shape[0] / 6)))
|
||
# sub_im = cv2.resize(sub_im, (int(sub_im.shape[1]/10), int(sub_im.shape[0]/10)))
|
||
cv2.imshow("2", img_matches)
|
||
# cnts的矩形画在sub_im 上
|
||
# for i in range(len(cnts)):
|
||
# p = cnts[i]
|
||
# cv2.rectangle(sub_im, (p['x'], p['y']), (p['x']+p['width'],p['y']+p['height']), (0, 0, 255), 2)
|
||
# # 写编号
|
||
# cv2.putText(sub_im, str(i), (p['x'], p['y']), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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# cv2.imshow("sub_im_after_filter", sub_im)
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cv2.waitKey(0)
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