198 lines
6.6 KiB
Python
198 lines
6.6 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@project:
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@File : calculate
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@Author : qiqq
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@create_time : 2023/4/24 20:09
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一个区的图都分别村再不同的地方
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"""
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#统计结果中光伏或者屋顶像素的个数进而计算面积
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import os
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import json
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from PIL import Image
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import numpy as np
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from tqdm import tqdm
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def get_file_list(parent_dir, file_type, file_list):
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"""
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:param parent_dir: 想要获取文件列表的一级目录
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:param file_type: 想要获取的文件类型（后缀名）
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:param file_list: 将获取到的文件列表存储到该列表中
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:return: 返回获取到的文件列表
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"""
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# 列举出当前文件路径下的所有文件名，也可能是文件夹名
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for f in os.listdir(parent_dir):
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# 将当前路径下的文件名或文件夹名和上一级目录进行连接，进而获取到一个新的路径
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temp_dir = os.path.join(parent_dir, f)
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# 判断当前绝对路径的类型如果是一个文件而非文件夹且文件类型为指定的类型，则将该绝对路径添加到 file_list 文件列表中
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if os.path.isfile(temp_dir) and temp_dir.endswith(file_type):
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file_list.append(temp_dir)
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# 如果当前绝对路径的类型是一个文件夹，则递归调用该函数，直到将所有的文件递归获取并判断完毕
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elif os.path.isdir(temp_dir):
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get_file_list(temp_dir, file_type, file_list)
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# 返回文件列表 file_list
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return file_list
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def cal_pvarea2():
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'''根据gsd计算：gloablemaspper导出的0.3m
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'''
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liss=[]
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pv_path = "/h/"#光伏分割结果
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roof_path = "//" #屋顶分割结果分割结果
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hechengpath="//"#通过roofpv_generate.py合成的多目标图
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print("+++++++++++++++统计纯pv+++++++++++++++++")
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liss = []
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imagelist = get_file_list(parent_dir=pv_path,
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file_type="png",
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file_list=liss
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)
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print(f"共发现{len(imagelist)}张")
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pv_piex = 0
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for i in tqdm(imagelist):
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img = np.array(Image.open(i))
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current_pvpiex = np.count_nonzero(img)
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pv_piex += current_pvpiex
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print(f"共监测到光伏像素点数量{pv_piex}")
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gsd = round(0.26 * 0.26, 4)
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pvarea_m2 = round(float(pv_piex) * gsd, 4)
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print(f"{gsd}下的全部光伏面积为{pvarea_m2}m")
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convert=150
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capti=(convert*pvarea_m2)/1000 #w
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print("估计的光伏容量为",round(capti,4))
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print("+++++++++++++++统计纯roof+++++++++++++++")
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liss = []
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imagelist = get_file_list(parent_dir=roof_path,
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file_type="png",
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file_list=liss
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)
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print(f"共发现{len(imagelist)}张")
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roof_piex = 0
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for i in tqdm(imagelist):
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img = np.array(Image.open(i))
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current_roof_piex = np.count_nonzero(img)
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roof_piex += current_roof_piex
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print(f"共监测到屋顶像素点数量{roof_piex}")
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gsd = round(0.26 * 0.26, 4)
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roof_area_m2 = round(float(roof_piex) * gsd, 4)
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print(f"{gsd}下的全部屋顶面积为{roof_area_m2}m")
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print("+++++++++++++++统计合成+++++++++++++++")
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liss = []
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imagelist = get_file_list(parent_dir=hechengpath,
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file_type="png",
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file_list=liss
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)
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'''
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r0 p0 -->背景 0 黑色
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r1 p0 --->屋顶 1(应该是空白屋顶了) #红色
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r0 p1 -->非屋顶光伏 2 #绿色
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r1 p1--- 屋顶光伏 3 #蓝色
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'''
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print(f"共发现{len(imagelist)}张")
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pv_piex = 0
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roofpv_piex = 0
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nroofpv_piex = 0
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roofwopv=0
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for i in tqdm(imagelist):
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img = np.array(Image.open(i))
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current_nroofpvpiex = np.sum(img == 2)
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current_roofpvpiex = np.sum(img == 3)
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current_pvpiex= current_nroofpvpiex+current_roofpvpiex
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roofpv_piex+=current_roofpvpiex
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nroofpv_piex+=current_nroofpvpiex
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pv_piex+=current_pvpiex
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current_roofwopv = np.sum(img == 1)
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roofwopv+=current_roofwopv
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print(f"共监测到光伏像素点数量{pv_piex}")
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print(f"共监测到非屋顶光伏像素点数量{nroofpv_piex}")
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print(f"共监测到屋顶光伏像素点数量{roofpv_piex}")
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print(f"共监测到剩余屋顶光伏像素点数量{roofwopv}")
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gsd = round(0.26 * 0.26, 4)
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roofpvarea_m2 = round(float(roofpv_piex) * gsd, 4)
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nroofpvarea_m2 = round(float(nroofpv_piex) * gsd, 4)
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pvarea_m2 = round(float(pv_piex) * gsd, 4)
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roofwopv_m2 = round(float(roofwopv) * gsd, 4)
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print(f"{gsd}下的屋顶光伏面积为{roofpvarea_m2}m")
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print(f"{gsd}下的非屋顶光伏面积为{nroofpvarea_m2}m")
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print(f"{gsd}下的剩余屋顶面积为{roofwopv_m2}m")
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print(f"{gsd}下的全部光伏面积为{pvarea_m2}m")
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cal_pvarea2()
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def cvtColor(image):
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if len(np.shape(image)) == 3 and np.shape(image)[2] == 3:
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return image
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else:
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image = image.convert('RGB')
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return image
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def get_imgs_vailidpiex():
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basedir = r"/home/qiqq/q3dl/applicationdata/beijing/tz"
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imglist=[]
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#全部模式
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lists1=[i for i in os.listdir(basedir) if not os.path.isfile(os.path.join(basedir, i))]#tz1tz2
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imagescomplete=[]
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for i in lists1: #tz1
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chidpath=os.path.join(basedir,i)#
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imageslist =[i for i in os.listdir(chidpath) if (not i.endswith(".xml")) and (not i.endswith("pgw"))]
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for k in imageslist:
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imagescomplepath=os.path.join(basedir,i,k)
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imagescomplete.append(imagescomplepath)
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print("共监测到{}张测试图像".format(len(imagescomplete)))
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vaildpixnum=0
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for j in tqdm(imagescomplete):
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# name=j.split(".")[0]
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name=j.split("/")[-1].split(".")[0]
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image=Image.open(j) #zhuyu
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orininal_h = image.size[1]
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orininal_w = image.size[0]
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image = cvtColor(image, )
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#
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image=image.convert('L')#转化层灰度图方便计算
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image=np.array(image)
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vailid=image[image!=255].size
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vaildpixnum=vaildpixnum+vailid
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# item={"name":name,"orininal_h":orininal_h,"orininal_w":orininal_w,"image":image}
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# imglist.append(item)
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print(f"有效得（非全255）得一共{vaildpixnum}")
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return vaildpixnum
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