82 lines
4.1 KiB
Python
82 lines
4.1 KiB
Python
from util import *
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import math
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import argparse
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def get_coor_base_interval(water_mask, severe_glint, output_csvpath, interval=100):
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dataset_water_mask = gdal.Open(water_mask)
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# geotransform_water_mask = dataset_water_mask.GetGeoTransform() # 仿射矩阵
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data_water_mask = dataset_water_mask.GetRasterBand(1).ReadAsArray()
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dataset_severe_glint = gdal.Open(severe_glint)
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data_severe_glint = dataset_severe_glint.GetRasterBand(1).ReadAsArray()
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im_width = dataset_severe_glint.RasterXSize # 栅格矩阵的列数
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im_height = dataset_severe_glint.RasterYSize # 栅格矩阵的行数
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geotransform_input = dataset_severe_glint.GetGeoTransform() # 仿射矩阵
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inv_geotransform_input = gdal.InvGeoTransform(geotransform_input)
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x_max = geotransform_input[0] + im_width * geotransform_input[1] + im_height * geotransform_input[2]
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y_min = geotransform_input[3] + im_width * geotransform_input[4] + im_height * geotransform_input[5]
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x_range = [geotransform_input[0], x_max]
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y_range = [y_min, geotransform_input[3]]
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spatial_resolution = geotransform_input[1] * interval
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dx = math.ceil((x_range[1] - x_range[0]) / spatial_resolution)
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dy = math.ceil((y_range[1] - y_range[0]) / spatial_resolution)
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geotransform_out = (x_range[0], spatial_resolution, 0, y_range[1], 0, -spatial_resolution)
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# 确定像元的投影范围
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valid_area_ = (data_water_mask > 0) & (~ (data_severe_glint > 0))
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valid_area = np.logical_and(data_water_mask, np.logical_not(data_severe_glint)) # 逻辑矩阵，true代表是水且不是耀斑
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valid_area_path = os.path.splitext(output_csvpath)[0] + "_valid_area"
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write_bands(water_mask, valid_area_path, valid_area)
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x_out = []
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y_out = []
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with open(output_csvpath, "w") as f:
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for row in range(dy):
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for column in range(dx):
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top_left = gdal.ApplyGeoTransform(geotransform_out, column, row) # (x,y)
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bottom_right = gdal.ApplyGeoTransform(geotransform_out, column + 1, row + 1)
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top_left2 = gdal.ApplyGeoTransform(inv_geotransform_input, top_left[0], top_left[1]) # (x,y)
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bottom_right2 = gdal.ApplyGeoTransform(inv_geotransform_input, bottom_right[0], bottom_right[1])
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top_left2 = [int(num) for num in top_left2]
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bottom_right2 = [int(num) for num in bottom_right2]
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valid_area_local = valid_area[top_left2[1]:bottom_right2[1], top_left2[0]:bottom_right2[0]] # numpy索引时(y,x)
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re = np.where(valid_area_local == True)
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coor_y_local, coor_x_local = getnearest(valid_area_local)
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if re[0].shape[0] > 0:
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x = top_left2[0] + int(re[1][0])
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y = top_left2[1] + int(re[0][0])
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x = top_left2[0] + coor_x_local
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y = top_left2[1] + coor_y_local
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# 将坐标和对应像元的光谱写入txt文件
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dest_coordinate = gdal.ApplyGeoTransform(geotransform_input, x, y) # (x,y)
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f.write("%s\t %s\t %s\t %s\n" % (str(dest_coordinate[0]), str(dest_coordinate[1]), str(x), str(y)))
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x_out.append(dest_coordinate[0])
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y_out.append(dest_coordinate[1])
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return x_out, y_out
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# Press the green button in the gutter to run the script.
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="此程序通过给定间隔输出坐标。")
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parser.add_argument('-i1', '--water_mask', type=str, required=True, help='输入水域掩膜文件的路径')
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parser.add_argument('-i2', '--severe_glint', type=str, required=True, help='输入耀斑严重文件的路径')
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parser.add_argument('-i3', '--interval', type=float, required=True, help='输入间隔，单位为米')
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parser.add_argument('-o', '--output_csvpath', type=str, required=True, help='输出csv文件的路径')
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parser.add_argument('-v', '--verbose', action='store_true', help='启用详细模式')
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args = parser.parse_args()
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tmp = get_coor_base_interval(args.water_mask, args.severe_glint, args.output_csvpath, args.interval)
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