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2D_Hausdorff_performance.py
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from scipy.spatial.distance import directed_hausdorff
import codecs
import numpy as np
import os
import re
# =========================================
#
# Original data Concave Hull
#
# =========================================
orig_data_path = "C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/FA/txt_file/" # 原始数据的路径
name = "pier_test.txt" # 原始数据
# =========================================
# Calculate Hausdorff
# =========================================
f = codecs.open(orig_data_path + name, mode='r', encoding='utf-8') # 打开txt文件，以‘utf-8’编码读取,这里读取的是利用get_concave_vertex得到的vertex点
line = f.readline() # 以行的形式进行读取文件
list1 = []
while line:
a = line.split()
b = a[0:2] # 这是选取需要读取的位数 前两列 X,Y
list1.append(b) # 将其添加在列表之中
line = f.readline()
f.close()
x = [float(i[0]) for i in list1]
y = [float(i[1]) for i in list1]
list_orig = np.array(list1)
file_name = 'Orig_2D_Hausdorff_Results.txt'
f1 = open(orig_data_path + file_name, 'a')
f1.write("way 2DHausdorff\n")
f1.close()
# =========================================
#
# Random data Hausdorff
#
# =========================================
# 读取所有Random开头的文件
random_files = []
data_dir = "C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/Oct_Rand_Sample_1/"
for i in os.listdir(data_dir):
if os.path.isfile(os.path.join(data_dir,i)) and 'Random' in i:
random_files.append(i)
seed_count = 0
file_name = 'rand_2D_Hausdorff_Results.txt'
f = open(data_dir + file_name, 'a')
for random_file in random_files:
print(random_file)
# =========================================
# Calculate Hausdorff
# =========================================
f = codecs.open('C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/Oct_Rand_Sample_1/' + str(random_file[0:]),
mode='r', encoding='utf-8') # 打开txt文件，以‘utf-8’编码读取,这里读取的是利用get_concave_vertex得到的vertex点
line = f.readline() # 以行的形式进行读取文件
list1 = []
while line:
a = line.split()
b = a[0:2] # 这是选取需要读取的位数 前两列 X,Y
list1.append(b) # 将其添加在列表之中
line = f.readline()
f.close()
x = [float(i[0]) for i in list1]
y = [float(i[1]) for i in list1]
list_random = np.array(list1)
h_dist = max(directed_hausdorff(list_orig, list_random)[0], directed_hausdorff(list_random, list_orig)[0])
seed_value_list = [int(x) for x in re.findall('\d+', random_file)]
seed_value = seed_value_list[2]
# =========================================
# Write hausdorff results into file
# =========================================
file_line = ''
seed_count += 1
print(seed_count)
file_line += ("{} {}\n".format(seed_value, h_dist))
file_name = 'rand_2D_Hausdorff_Results.txt'
f = open(data_dir + file_name, 'a')
f.write(file_line)
f.close()
# =========================================
#
# Octree and BDSR data Hausdoff
#
# =========================================
cr1 = "0.05" # 一压压缩率
dataset = name.replace(".txt","")
WAY_list = ["FA", "PCA", "KernelPCA", "TruncatedSVD", "octreeDownsampling", "octreeResampling"]
for way in WAY_list:
if way == "FA":
way_folder = "FA/txt_file1"
data_name = way + "_down_" + cr1 + "_0.3_PCD.txt"
elif way == "PCA":
way_folder = "PCA/txt_file1"
data_name = way + "_down_" + cr1 + "_0.3_PCD.txt"
elif way == "KernelPCA":
way_folder = "KernelPCA/txt_file1"
data_name = way + "_down_" + cr1 + "_0.3_PCD.txt"
elif way == "TruncatedSVD":
way_folder = "TruncatedSVD/txt_file1"
data_name = way + "_down_" + cr1 + "_0.3_PCD.txt"
elif way == "octreeDownsampling":
way_folder = "Oct_Rand_Sample_1"
data_name = dataset + "_" + way + ".txt"
else:
way_folder = "Oct_Rand_Sample_1"
data_name = dataset + "_" + way + ".txt"
data_dir = "C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/" + way_folder + "/"
# =========================================
# Calculate Hausdorff
# =========================================
f = codecs.open(data_dir + data_name, mode='r', encoding='utf-8') # 打开txt文件，以‘utf-8’编码读取,这里读取的是利用get_concave_vertex得到的vertex点
line = f.readline() # 以行的形式进行读取文件
list1 = []
while line:
a = line.split()
b = a[0:2] # 这是选取需要读取的位数 前两列 X,Y
list1.append(b) # 将其添加在列表之中
line = f.readline()
f.close()
x = [float(i[0]) for i in list1]
y = [float(i[1]) for i in list1]
list_not_random = np.array(list1)
h_dist = max(directed_hausdorff(list_orig, list_not_random)[0], directed_hausdorff(list_not_random, list_orig)[0])
# =========================================
# Write Hausdorff results into file
# =========================================
file_name = '{}_2D_Hausdorff_Results.txt'.format(way)
f = open(data_dir + file_name, 'a')
file_line = ""
file_line += ("{} {}\n".format(way, h_dist))
f.write(file_line)
f.close()
# =================================================
#
# Concatenate files and generate final result
#
# =================================================
orig_hausd_file = "C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/FA/txt_file/Orig_2D_Hausdorff_Results.txt"
FA_hausd_file = "C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/FA/txt_file1/FA_2D_Hausdorff_Results.txt"
PCA_hausd_file = "C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/PCA/txt_file1/PCA_2D_Hausdorff_Results.txt"
KernelPCA_hausd_file = "C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/KernelPCA/txt_file1/KernelPCA_2D_Hausdorff_Results.txt"
TruncatedSVD_hausd_file = "C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/TruncatedSVD/txt_file1/TruncatedSVD_2D_Hausdorff_Results.txt"
octDown_hausd_file = "C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/Oct_Rand_Sample_1/octreeDownsampling_2D_Hausdorff_Results.txt"
octRe_hausd_file = "C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/Oct_Rand_Sample_1/octreeResampling_2D_Hausdorff_Results.txt"
random_hausd_file = "C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/Oct_Rand_Sample_1/rand_2D_Hausdorff_Results.txt"
hausd_list = [orig_hausd_file, FA_hausd_file, PCA_hausd_file, KernelPCA_hausd_file, TruncatedSVD_hausd_file, octDown_hausd_file, octRe_hausd_file, random_hausd_file]
with open('C:/Users/cvrl/Documents/PCD_projects/Results/BDSR/Performance/2D_Hausdorff_Results.txt', 'w') as outfile:
for hausd in hausd_list:
with open(hausd) as infile:
outfile.write(infile.read())
outfile.close()