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main_get_shp_area.py
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main_get_shp_area.py
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# This is a program used to obtain lake water body and lake area from water index images(tif)
# water index images (tif) are available on the GEE platform
# The method of selecting the segmentation threshold of lake water body is OTSU
from Index_processing import quyun, ostu, lvbo
import os
import numpy as np
from read_write_img import read_img, write_img
import cv2
from GSW_tif2shp2area import tif2shp, area
import pandas as pd
import time
import shutil
# This is UTM projection information.
# Qaidam Basin contains UTM46 and UTM47
# Available from https://spatialreference.org/
img_proj_UTM46 = '''PROJCS["WGS 84 / UTM zone 46N",
GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.01745329251994328,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4326"]],
UNIT["metre",1,
AUTHORITY["EPSG","9001"]],
PROJECTION["Transverse_Mercator"],
PARAMETER["latitude_of_origin",0],
PARAMETER["central_meridian",93],
PARAMETER["scale_factor",0.9996],
PARAMETER["false_easting",500000],
PARAMETER["false_northing",0],
AUTHORITY["EPSG","32646"],
AXIS["Easting",EAST],
AXIS["Northing",NORTH]]'''
img_proj_UTM47 = '''PROJCS["WGS 84 / UTM zone 47N",
GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.01745329251994328,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4326"]],
UNIT["metre",1,
AUTHORITY["EPSG","9001"]],
PROJECTION["Transverse_Mercator"],
PARAMETER["latitude_of_origin",0],
PARAMETER["central_meridian",99],
PARAMETER["scale_factor",0.9996],
PARAMETER["false_easting",500000],
PARAMETER["false_northing",0],
AUTHORITY["EPSG","32647"],
AXIS["Easting",EAST],
AXIS["Northing",NORTH]]'''
def water_index_filter(after_quyun_data):
for i in range(1, after_quyun_data.shape[0] - 1):
for j in range(1, after_quyun_data.shape[1] - 1):
if abs(after_quyun_data[i][j] < 1):
count = 1
filter = after_quyun_data[i-count:i+(count+1), j-count:j+(count+1)]
filter[filter > 1] = np.nan
filter[filter < -1] = np.nan
after_quyun_data[i][j] = np.nanmean(filter)
if after_quyun_data[i][j] == np.nan:
count += 1
filter1 = after_quyun_data[i - count:i + (count + 1), j - count:j + (count + 1)]
filter1[filter1 > 1] = np.nan
filter1[filter1 < -1] = np.nan
after_quyun_data[i][j] = np.nanmean(filter)
return after_quyun_data
def all_area_shp(file_path, alt_path):
global img_proj, shapefile_other1_list, shapefile_other2_list
alt_sign = os.path.basename(os.path.dirname(alt_path))
img_sign = os.path.basename(os.path.dirname(file_path)).split('_')[-1]
print(img_sign, alt_sign)
if alt_sign == 'CryoSat2':
file_path = os.path.dirname(file_path) + '/match_CS'
elif alt_sign == 'ICESat2':
file_path = os.path.dirname(file_path) + '/match_ICE2'
elif alt_sign == 'Sentinel3':
file_path = os.path.dirname(file_path) + '/match_ST3'
else:
print('Unrecognized altimetry data!')
shapefile_sign_list = ['ST3', 'ICE2', 'CS']
# print(shapefile_sign_list)
shapefile_sign = str(os.path.basename(file_path).split('_')[-1])
# print(shapefile_sign)
shapefile_sign_list.remove(shapefile_sign)
other1_shapefile_sign = shapefile_sign_list[0]
other2_shapefile_sign = shapefile_sign_list[1]
# print(other2_shapefile_sign)
file_name_list = os.listdir(file_path)
# result_path = 'J:/Remote_Sensing_Image/tuosu/Tuosu_landsat/quyun/'
write_img_path = os.path.dirname(file_path) + '/quyun_' + shapefile_sign
# 'J:/Remote_Sensing_Image/tuosu/Tuosu_landsat/OTSU'
write_OTSU_path = os.path.dirname(file_path) + '/OTSU_' + shapefile_sign
# result_file_path2 = 'J:/Remote_Sensing_Image/tuosu/Tuosu_landsat/color'
write_color_path = os.path.dirname(file_path) + '/color_' + shapefile_sign
shapefile = os.path.join(os.path.dirname(file_path), 'shapefile_' + shapefile_sign)
if not os.path.exists(os.path.join(shapefile)):
os.makedirs(os.path.join(shapefile))
if not os.path.exists(os.path.join(write_img_path)):
os.makedirs(os.path.join(write_img_path))
if not os.path.exists(os.path.join(write_OTSU_path)):
os.makedirs(os.path.join(write_OTSU_path))
if not os.path.exists(os.path.join(write_color_path)):
os.makedirs(os.path.join(write_color_path))
date_list = []
shapefile_other1 = os.path.join(os.path.dirname(file_path), 'shapefile_' + other1_shapefile_sign)
shapefile_other2 = os.path.join(os.path.dirname(file_path), 'shapefile_' + other2_shapefile_sign)
try:
shapefile_other1_list = os.listdir(shapefile_other1)
shapefile_other2_list = os.listdir(shapefile_other2)
except:
print('')
for file_name in file_name_list:
print('File name', file_name)
date = file_name[: -4]
date_list.append(date)
# If shp exists in other shapefiles, copy it directly
if os.path.exists(shapefile_other1):
if date in shapefile_other1_list:
print('shapefile_' + other1_shapefile_sign + 'Existing file, copy to shapefile_' + shapefile_sign)
if not os.path.exists(os.path.join(shapefile, str(date))):
shutil.copytree(os.path.join(shapefile_other1, str(date)),
os.path.join(shapefile, str(date)))
try:
if os.path.exists(shapefile_other2):
if date in shapefile_other2_list:
print('shapefile_' + other2_shapefile_sign + 'Existing file, copy to shapefile_' + shapefile_sign)
if not os.path.exists(os.path.join(shapefile, str(date))):
shutil.copytree(os.path.join(shapefile_other2, str(date)),
os.path.join(shapefile, str(date)))
except:
print('')
if not os.path.exists(os.path.join(shapefile, str(date))):
img_proj = OTSU_img(file_path, file_name, shapefile_sign, write_img_path, write_OTSU_path, write_color_path, img_sign)
else:
print('File already exists')
shapefile_ST3_list = os.listdir(shapefile)
# print(shapefile_ST3_list)
try:
lake_area(file_path, shapefile_ST3_list, shapefile_sign, img_proj)
except NameError:
_, im_geotrans2, image_slave = read_img(os.path.join(file_path, os.listdir(file_path)[0]))
print(os.path.join('/'.join(file_path.split('/')[0: -2]), 'Location.txt'))
if not os.path.exists(os.path.join('/'.join(file_path.split('/')[0: -2]), 'Location.txt')):
location = [im_geotrans2[3], im_geotrans2[3] + im_geotrans2[5] * image_slave.shape[0], im_geotrans2[0],
im_geotrans2[0] + im_geotrans2[1] * image_slave.shape[1]]
# print(location)
np.savetxt(os.path.join('/'.join(file_path.split('/')[0: -2]), 'Location.txt'), location)
if im_geotrans2[0] + im_geotrans2[1] + image_slave.shape[1] > 96.1:
img_proj = img_proj_UTM47
else:
img_proj = img_proj_UTM46
lake_area(file_path, shapefile_ST3_list, shapefile_sign, img_proj)
def OTSU_img(file_path, file_name, shp_sign, write_img_path, write_OTSU_path, write_color_path, img_sign):
im_proj2, im_geotrans2, image_slave = read_img(os.path.join(file_path, file_name))
if not os.path.exists(os.path.join('/'.join(file_path.split('/')[0: -2]), 'Location.txt')):
location = [im_geotrans2[3], im_geotrans2[3] + im_geotrans2[5] * image_slave.shape[0], im_geotrans2[0],
im_geotrans2[0] + im_geotrans2[1] * image_slave.shape[1]]
# print(location)
np.savetxt(os.path.join('/'.join(file_path.split('/')[0: -2]), 'Location.txt'), location)
if im_geotrans2[0] + im_geotrans2[1]*image_slave.shape[1] > 96.1:
img_proj = img_proj_UTM47
else:
img_proj = img_proj_UTM46
# print(img_proj)
if (len(image_slave[np.isnan(image_slave)])) / (image_slave.shape[0] * image_slave.shape[1]) < 0.8:
print('Meet the cloud cover conditions,denoising...')
if img_sign == 'Landsat7':
quyun_image_slave = quyun(image_slave)
# 确保NDWI的范围在-1-1之间
# quyun_image_slave = NDWI_filter(quyun_image_slave)
quyun_image_slave[quyun_image_slave > 1] = 0.5
quyun_image_slave[quyun_image_slave < -1] = -0.5
# # 一般情况下，landsat7加滤波
quyun_image_slave = lvbo(quyun_image_slave)
else:
image_slave[image_slave > 1] = 0.5
image_slave[image_slave < -1] = -0.5
quyun_image_slave = image_slave
else:
quyun_image_slave = np.random.rand(image_slave.shape[0], image_slave.shape[1])
# print(quyun_image_slave)
# print(quyun_image_slave)
print('Excessive cloudiness')
write_img(os.path.join(write_img_path, file_name[: -4] + '_CR.tif'), im_proj2, im_geotrans2, quyun_image_slave)
print('Denoising is complete, OTSU is automatically segmenting...')
dst_Otsu, best_thresold_NDWI, dst = ostu(quyun_image_slave)
print('The segmentation is complete, it is being converted to a shp file...')
tif2shp(os.path.join(write_img_path, file_name[: -4] + '_CR.tif'), best_thresold_NDWI,
os.path.join(os.path.dirname(write_img_path), 'shapefile_' + shp_sign, file_name[: -4]), os.path.basename(file_path))
print('The shapefile conversion is complete!')
cv2.imwrite(os.path.join(write_OTSU_path, file_name[: -4] + '_OTSU.tif'), dst_Otsu)
cv2.imwrite(os.path.join(write_color_path, file_name[: -4] + '_color.tif'), dst)
return img_proj
def lake_area(file_path, shapefile_ST3_list, img_sign, img_proj):
lake_area_list = []
date_list = []
for shp_name in shapefile_ST3_list:
# print(os.path.join(shp_path, shp_name, shp_name + '.shp'))
# print(os.path.join(os.path.join(os.path.dirname(file_path), 'shapefile_' + img_sign)))
lake_area = area(os.path.join(os.path.join(os.path.dirname(file_path), 'shapefile_' + img_sign), shp_name, shp_name + '.shp'), img_proj)
lake_area_list.append(lake_area)
date_list.append(shp_name)
data = pd.DataFrame(lake_area_list)
data.columns = ['lake_area']
data.insert(0, 'date', date_list)
writer = pd.ExcelWriter(os.path.join(os.path.abspath(os.path.dirname(file_path)), 'lake_area_' + img_sign + '.xlsx'))
data.to_excel(writer, header=True, index=False, sheet_name='lake_area')
writer.save()
if __name__ == '__main__':
# Folder for storing water index(tif)
file_path = ''
# Folder for storing satellite altimetry data
alt_path = ''
start = time.time()
all_area_shp(file_path, alt_path)
end = time.time()
print(end-start)