561 lines
26 KiB
Python
561 lines
26 KiB
Python
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from tool.algorithm.transforml1a import SAR_GEO as SAR_GEO
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from tool.algorithm.image import ImageHandle
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import numpy as np
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import scipy
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from scipy.interpolate import griddata, RegularGridInterpolator
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import logging
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import pyresample as pr
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# 插值模块
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from pyresample.bilinear import NumpyBilinearResampler
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from pyresample import geometry
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from pyresample.geometry import AreaDefinition
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from osgeo import osr
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import os
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# os.environ['PROJ_LIB'] = r"D:\Anaconda\envs\micro\Lib\site-packages\osgeo\data\proj"
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logger = logging.getLogger("mylog")
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##############
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# 多项式回归组件
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##############
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#
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def griddata_geo(points, data, lon_grid, lat_grid, method, i, end_i):
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grid_data = griddata(points, data, (lon_grid, lat_grid), method=method, )
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grid_data = grid_data[:, :, 0]
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return [i, end_i, grid_data]
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def griddataBlock(start_x, len_x, start_y, len_y, grid_data_input, grid_x, grid_y, method):
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grid_x = grid_x.reshape(-1)
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grid_y = grid_y.reshape(-1)
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grid_data_input = grid_data_input.reshape(-1)
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x_list = np.array(list(range(len_x))) + start_x
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y_list = np.array(list(range(len_y))) + start_y
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x_grid, y_grid = np.meshgrid(x_list, y_list)
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idx = np.argsort(grid_x)
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grid_x = grid_x[idx].reshape(-1)
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grid_y = grid_y[idx].reshape(-1)
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grid_data_input = grid_data_input[idx].reshape(-1)
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interp_func = RegularGridInterpolator((grid_x.reshape(-1), grid_y.reshape(-1)), grid_data_input.reshape(-1),
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method='slinear', bounds_error=False, fill_value=np.nan)
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grid_data = interp_func((x_grid, y_grid))
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# grid_data = griddata(p, grid_data_input, (x_grid, y_grid), method=method)
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grid_data = grid_data[:, :, 0]
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return (x_grid, y_grid, grid_data)
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class polyfit2d_U:
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def __init__(self, x, y, z) -> None:
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# 定义参数
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X = np.ones((x.shape[0], 10))
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X[:, 0] = 1
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X[:, 1] = x
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X[:, 2] = y
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X[:, 3] = x * y
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X[:, 4] = x ** 2
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X[:, 5] = y ** 2
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X[:, 6] = x * X[:, 5]
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X[:, 7] = y * X[:, 4]
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X[:, 8] = x ** 3
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X[:, 9] = y ** 3
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Y = z.reshape(-1, 1)
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A = np.matmul(np.matmul(np.linalg.inv(np.matmul(X.T, X)), X.T), Y)
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self.paras_fit = A
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def fit(self, x, y):
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X = np.ones((x.shape[0], 10))
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X[:, 0] = 1
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X[:, 1] = x
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X[:, 2] = y
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X[:, 3] = x * y
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X[:, 4] = x ** 2
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X[:, 5] = y ** 2
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X[:, 6] = x * X[:, 5]
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X[:, 7] = y * X[:, 4]
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X[:, 8] = x ** 3
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X[:, 9] = y ** 3
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z = np.matmul(X, self.paras_fit)
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return np.sum(z)
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class TransImgL1A:
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def __init__(self, ori_sim_path, roi):
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self._begin_r, self._begin_c, self._end_r, self._end_c = 0, 0, 0, 0
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self._mask = None
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self._min_lon, self._max_lon, self._min_lat, self._max_lat = 0, 0, 0, 0
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self.init_trans_para(ori_sim_path, roi)
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def get_roi_points(self):
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rowcol = np.where(self._mask == 1)
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data = [(self._begin_r + row, self._begin_c + col) for (row, col) in zip(rowcol[0], rowcol[1])]
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return data
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######################
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# 插值方法
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######################
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def init_trans_para(self, ori_sim_path, roi):
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"""裁剪L1a_img
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Args:
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src_img_path (_type_): 原始L1A影像
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cuted_img_path (_type_): 待裁剪对象
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roi (_type_): 裁剪roi
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"""
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ori2geo_img = ImageHandle.ImageHandler.get_data(ori_sim_path)
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point_list = np.array(roi)
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min_lon = np.nanmin(point_list[:, 0])
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max_lon = np.nanmax(point_list[:, 0])
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min_lat = np.nanmin(point_list[:, 1])
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max_lat = np.nanmax(point_list[:, 1])
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self._min_lon, self._max_lon, self._min_lat, self._max_lat = min_lon, max_lon, min_lat, max_lat
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r_c_list = np.where(
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(ori2geo_img[0, :, :] >= min_lon) & (ori2geo_img[0, :, :] <= max_lon)
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& (ori2geo_img[1, :, :] >= min_lat) & (ori2geo_img[1, :, :] <= max_lat)) #
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if len(r_c_list) == 0 or r_c_list[0] == [] or r_c_list[1] == [] or np.array(r_c_list).size == 0:
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msg = 'csv_roi:' + str(roi) + 'not in box,please revise csv data！'
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print(msg)
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else:
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# print("csv_roi:")
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# print(roi)
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r_min = np.nanmin(r_c_list[0])
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r_max = np.nanmax(r_c_list[0])
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c_min = np.nanmin(r_c_list[1])
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c_max = np.nanmax(r_c_list[1])
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ori2geo_img = ori2geo_img[:, r_min:r_max + 1, c_min:c_max + 1]
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# 开始调用组件 计算
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mask = SAR_GEO.cut_L1A_img(ori2geo_img.astype(np.float64), point_list)
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self._begin_r = r_min
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self._end_r = r_max
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self._begin_c = c_min
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self._end_c = c_max
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self._mask = mask
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def cut_L1A(self, in_path, out_path):
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img = ImageHandle.ImageHandler.get_data(in_path)
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if len(img.shape) == 3:
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cut_img = img[:, self._begin_r:self._end_r + 1, self._begin_c:self._end_c + 1]
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cut_img[0, :, :] = cut_img[0, :, :] * self._mask
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cut_img[1, :, :] = cut_img[1, :, :] * self._mask
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ImageHandle.ImageHandler.write_img(out_path, '', [0, 0, 0, 0, 0, 0], cut_img)
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else:
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cut_img = img[self._begin_r:self._end_r + 1, self._begin_c:self._end_c + 1]
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cut_img[:, :] = cut_img[:, :] * self._mask
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cut_img[:, :] = cut_img[:, :] * self._mask
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ImageHandle.ImageHandler.write_img(out_path, '', [0, 0, 0, 0, 0, 0], cut_img)
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def grid_interp_to_station(self, all_data, station_lon, station_lat, method='linear'):
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'''
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func: 将等经纬度网格值 插值到 离散站点。使用griddata进行插值
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inputs:
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all_data,形式为：[grid_lon,grid_lat,data] 即[经度网格，纬度网格，数值网格]
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station_lon: 站点经度
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station_lat: 站点纬度。可以是 单个点，列表或者一维数组
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method: 插值方法,默认使用 linear
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'''
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station_lon = np.array(station_lon).reshape(-1, 1)
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station_lat = np.array(station_lat).reshape(-1, 1)
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lon = all_data[0].reshape(-1, 1)
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lat = all_data[1].reshape(-1, 1)
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data = all_data[2].reshape(-1, 1)
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points = np.concatenate([lon, lat], axis=1)
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station_value = griddata(points, data, (station_lon, station_lat), method=method)
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station_value = station_value[:, :, 0]
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return station_value
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#####################
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# 当存在 ori2geo.tif
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#####################
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@staticmethod
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def cut_L1a_img(src_img_path, cuted_img_path, roi):
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"""裁剪L1a_img
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Args:
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src_img_path (_type_): 原始L1A影像
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cuted_img_path (_type_): 待裁剪对象
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roi (_type_): 裁剪roi
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"""
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ori2geo_img = ImageHandle.ImageHandler.get_data(src_img_path)
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point_list = np.array(roi)
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# 开始调用组件 计算
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mask = SAR_GEO.cut_L1A_img(ori2geo_img.astype(np.float64), point_list)
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#
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ori2geo_img[0, :, :] = ori2geo_img[0, :, :] * mask
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ori2geo_img[1, :, :] = ori2geo_img[1, :, :] * mask
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ImageHandle.ImageHandler.write_img(cuted_img_path, '', [0, 0, 0, 0, 0, 0], ori2geo_img)
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return ori2geo_img # 保存成影像
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def tran_geo_to_l1a(self, geo_img_path, out_l1a_img_path, ori_sim_img_path, is_class=False):
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"""裁剪后的有投影信息的影像(cover、ndvi)转换到L1A裁剪影像的尺寸
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Args:
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geo_img_path (_type_): _description_
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out_l1a_img_path (_type_): _description_
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ori_sim_img_path (_type_): _description_
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geo_img_path:地理影像路径
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out_l1a_img_path：转换L1A坐标系图像路径
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ori_sim_img_path：裁剪后模拟影像路径
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is_clss: 是否是 定性类产品
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"""
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inverse_gt = ImageHandle.ImageHandler.get_invgeotransform(geo_img_path)
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ori2geo_tif = ImageHandle.ImageHandler.get_data(ori_sim_img_path)
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height = ImageHandle.ImageHandler.get_img_height(geo_img_path)
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width = ImageHandle.ImageHandler.get_img_width(geo_img_path)
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# 计算投影
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x = ori2geo_tif[0, :, :] # lon lat x,y
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y = ori2geo_tif[1, :, :]
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ori2geo_tif[0, :, :] = inverse_gt[0] + inverse_gt[1] * x + inverse_gt[2] * y # x
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ori2geo_tif[1, :, :] = inverse_gt[3] + inverse_gt[4] * x + inverse_gt[5] * y # y
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del x, y
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geo_tif = ImageHandle.ImageHandler.get_data(geo_img_path) # 获取目标影像
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ori2geo_tif_shape = ori2geo_tif.shape # height,width
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if is_class:
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ori2geo_tif = np.round(ori2geo_tif).astype(np.int32)
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mask = (ori2geo_tif[0, :, :] >= 0) & (ori2geo_tif[0, :, :] < width) & (ori2geo_tif[1, :, :] >= 0) & (
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ori2geo_tif[1, :, :] < height)
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ori2geo_tif[0, :, :] = ori2geo_tif[0, :, :] * mask
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ori2geo_tif[1, :, :] = ori2geo_tif[1, :, :] * mask
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geo_tif_shape = geo_tif.shape
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geo_tif_l1a = geo_tif[ori2geo_tif[1, :, :].reshape(-1), ori2geo_tif[0, :, :].reshape(-1)].reshape(
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ori2geo_tif.shape[1], ori2geo_tif.shape[2]).astype(np.float32)
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del ori2geo_tif, geo_tif
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one_ids = np.where(mask == False)
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geo_tif_l1a[one_ids[0], one_ids[1]] = np.nan
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ImageHandle.ImageHandler.write_img(out_l1a_img_path, '', [0, 0, 0, 0, 0, 0], geo_tif_l1a)
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# save_temp_L1A(out_l1a_img_path,geo_tif_l1a)
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return geo_tif_l1a
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else: # 数值性插值
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mask = (ori2geo_tif[0, :, :] > 0) & (ori2geo_tif[0, :, :] < width - 1) & (ori2geo_tif[1, :, :] > 0) & (
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ori2geo_tif[1, :, :] < height - 1)
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one_ids = np.where(mask == 1)
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x, y = np.meshgrid(np.arange(0, width), np.arange(0, height))
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result_data = self.grid_interp_to_station([y.reshape(-1), x.reshape(-1), geo_tif.reshape(-1)],
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ori2geo_tif[1, one_ids[0], one_ids[1]].reshape(-1),
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ori2geo_tif[0, one_ids[0], one_ids[1]].reshape(-1),
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method='linear').reshape(-1)
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mask = mask.reshape(-1)
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result_data_result = np.zeros((ori2geo_tif.shape[1], ori2geo_tif.shape[2]))
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result_data_result[:, :] = np.nan
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result_data_result = SAR_GEO.insert_data(result_data_result, one_ids[0].astype(np.int32),
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one_ids[1].astype(np.int32), result_data)
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ImageHandle.ImageHandler.write_img(out_l1a_img_path, '', [0, 0, 0, 0, 0, 0], result_data_result)
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# save_temp_L1A(out_l1a_img_path,result_data_result)
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return result_data_result
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def tran_lonlats_to_rowcols(self, lonlats, ori_sim_img_path):
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"""
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功能：输入经纬度坐标，输出图像行列号
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函数名称：tran_lonlats_to_rowcols(lonlats,out_rowcols,ori_sim_img_path)
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Lonlats:经纬度坐标，示例[[120.53, 31.5], [120.61, 31.5], [120.53, 31.45], [120.61, 31.45]]
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out_rowcols:图像行列号，示例[[0, 0], [7000, 0], [7000, 8000], [0, 8000]]
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ori_sim_img_path：裁剪后模拟影像路径
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"""
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ori2geo_tif = ImageHandle.ImageHandler.get_data(ori_sim_img_path)
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min_lon = np.nanmin(ori2geo_tif[0, :, :])
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max_lon = np.nanmax(ori2geo_tif[0, :, :])
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min_lat = np.nanmin(ori2geo_tif[1, :, :])
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max_lat = np.nanmax(ori2geo_tif[1, :, :])
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result = []
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for i in range(len(lonlats)):
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p = lonlats[i]
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|
|
if min_lon > p[0] or max_lon < p[0] or min_lat > p[1] or max_lat < p[1]:
|
|||
|
|
result.append([-1, -1])
|
|||
|
|
continue
|
|||
|
|
temp_x = np.square(ori2geo_tif[0, :, :] - p[0]) + np.square(ori2geo_tif[1, :, :] - p[1])
|
|||
|
|
r_c_list = []
|
|||
|
|
r_c = np.argmin(temp_x)
|
|||
|
|
r_c = [r_c // temp_x.shape[1], r_c % temp_x.shape[1]]
|
|||
|
|
r_c_list.append([r_c[0], r_c[1], ori2geo_tif[0, r_c[0], r_c[1]], ori2geo_tif[1, r_c[0], r_c[1]]])
|
|||
|
|
# 插值
|
|||
|
|
for i in range(r_c[0] - 3, r_c[0] + 3):
|
|||
|
|
if i < 0 or i > temp_x.shape[0] - 1:
|
|||
|
|
continue
|
|||
|
|
for j in range(r_c[1] - 3, r_c[1] + 3):
|
|||
|
|
if j < 0 or j > temp_x.shape[1] - 1:
|
|||
|
|
continue
|
|||
|
|
r_c_list.append([i, j, ori2geo_tif[0, i, j], ori2geo_tif[1, i, j]])
|
|||
|
|
r_c_list = np.array(r_c_list)
|
|||
|
|
points = r_c_list[:, 2:]
|
|||
|
|
f_r = scipy.interpolate.interp2d(r_c_list[:, 2], r_c_list[:, 3], r_c_list[:, 0], kind='linear')
|
|||
|
|
f_c = scipy.interpolate.interp2d(r_c_list[:, 2], r_c_list[:, 3], r_c_list[:, 1], kind='linear')
|
|||
|
|
tar_get_r = f_r(p[0], p[1])[0]
|
|||
|
|
tar_get_c = f_c(p[0], p[1])[0]
|
|||
|
|
if tar_get_r < ori2geo_tif.shape[1] and tar_get_c < ori2geo_tif.shape[
|
|||
|
|
2] and tar_get_r >= 0 and tar_get_c >= 0:
|
|||
|
|
lon_temp = ori2geo_tif[0, int(round(tar_get_r)), int(round(tar_get_c))]
|
|||
|
|
lon_lat = ori2geo_tif[1, int(round(tar_get_r)), int(round(tar_get_c))]
|
|||
|
|
# 增加条件筛选
|
|||
|
|
result.append([tar_get_r, tar_get_c])
|
|||
|
|
else:
|
|||
|
|
result.append([-1, -1])
|
|||
|
|
return result
|
|||
|
|
|
|||
|
|
def tran_lonlats_to_L1A_rowcols(self, meas_data, ori_sim_path):
|
|||
|
|
lonlats = []
|
|||
|
|
data_roi = []
|
|||
|
|
for data in meas_data:
|
|||
|
|
lon = float(data[1])
|
|||
|
|
lat = float(data[2])
|
|||
|
|
if (lon > self._min_lon and lon < self._max_lon and lat > self._min_lat and lat < self._max_lat):
|
|||
|
|
lonlats.append([lon, lat])
|
|||
|
|
data_roi.append(data)
|
|||
|
|
|
|||
|
|
rowcols = self.tran_lonlats_to_rowcols(lonlats, ori_sim_path)
|
|||
|
|
measdata_list = []
|
|||
|
|
for data, rowcol in zip(data_roi, rowcols):
|
|||
|
|
if (rowcol[0] != -1 and rowcol[1] != -1):
|
|||
|
|
measdata_list.append(
|
|||
|
|
[round(rowcol[0]) - self._begin_r, round(rowcol[1]) - self._begin_c, float(data[3])])
|
|||
|
|
return measdata_list
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_radius_of_influence(lalo_step, src_meta='radar2geo', ratio=3):
|
|||
|
|
EARTH_RADIUS = 6378122.65 # m
|
|||
|
|
"""Get radius of influence based on the lookup table resolution in lat/lon direction"""
|
|||
|
|
if src_meta == "geo2radar":
|
|||
|
|
# geo2radar
|
|||
|
|
radius = 100e3
|
|||
|
|
else:
|
|||
|
|
# radar2geo
|
|||
|
|
step_deg = max(np.abs(lalo_step))
|
|||
|
|
step_m = step_deg * np.pi / 180.0 * EARTH_RADIUS
|
|||
|
|
radius = step_m * ratio
|
|||
|
|
return radius
|
|||
|
|
|
|||
|
|
def interp2d_station_to_grid(self, lon, lat, data, loc_range=[18, 54, 73, 135],
|
|||
|
|
det_grid=1, method='linear', projCode=4326):
|
|||
|
|
# 参考链接 https://blog.csdn.net/weixin_43718675/article/details/103497930
|
|||
|
|
'''
|
|||
|
|
func : 将站点数据插值到等经纬度格点
|
|||
|
|
inputs:
|
|||
|
|
lon: 站点的经度
|
|||
|
|
lat: 站点的纬度
|
|||
|
|
data: 对应经纬度站点的 气象要素值
|
|||
|
|
loc_range: [lat_min,lat_max,lon_min,lon_max]。站点数据插值到loc_range这个范围
|
|||
|
|
det_grid: 插值形成的网格空间分辨率
|
|||
|
|
method: 所选插值方法，默认 0.125
|
|||
|
|
return:
|
|||
|
|
|
|||
|
|
[lon_grid,lat_grid,data_grid]
|
|||
|
|
'''
|
|||
|
|
# step1: 先将 lon,lat,data转换成 n*1 的array数组
|
|||
|
|
lon = np.array(lon).reshape(-1, 1)
|
|||
|
|
lat = np.array(lat).reshape(-1, 1)
|
|||
|
|
data = np.array(data).reshape(-1, 1)
|
|||
|
|
|
|||
|
|
# step2:确定插值区域的经纬度网格
|
|||
|
|
lat_min = loc_range[0] # y
|
|||
|
|
lat_max = loc_range[1] # y
|
|||
|
|
lon_min = loc_range[2] # x
|
|||
|
|
lon_max = loc_range[3] # x
|
|||
|
|
gt = [0, 0, 0, 0, 0, 0]
|
|||
|
|
gt[0] = lon_min # x
|
|||
|
|
gt[1] = det_grid
|
|||
|
|
gt[3] = lat_max # y
|
|||
|
|
gt[5] = -det_grid
|
|||
|
|
lat_count = int((lat_max - lat_min) / det_grid + 1) # y
|
|||
|
|
lon_count = int((lon_max - lon_min) / det_grid + 1) # x
|
|||
|
|
# 替换为pyresample 插值方法
|
|||
|
|
proj_osr = osr.SpatialReference()
|
|||
|
|
proj_osr.ImportFromEPSG(projCode)
|
|||
|
|
projection = proj_osr.ExportToPROJJSON()
|
|||
|
|
# （lower_left_x、lower_left_y、upper_right_x、upper_right_y）
|
|||
|
|
target_def = AreaDefinition("id1", "WGS84", "proj_id", projection,
|
|||
|
|
lon_count, lat_count, [lon_min, lat_min, lon_max, lat_max])
|
|||
|
|
source_def = geometry.SwathDefinition(lons=lon, lats=lat)
|
|||
|
|
lalo_step = [det_grid, -det_grid]
|
|||
|
|
radius = TransImgL1A.get_radius_of_influence(lalo_step, src_meta='radar2geo')
|
|||
|
|
result = pr.bilinear.resample_bilinear(data, source_def, target_def,
|
|||
|
|
radius=radius, neighbours=32,
|
|||
|
|
nprocs=8, fill_value=np.nan,
|
|||
|
|
epsilon=0)
|
|||
|
|
#
|
|||
|
|
return result
|
|||
|
|
|
|||
|
|
def geocoding(self, ori_geo_tif, produc_arr, pixel_delta=1, method='linear'):
|
|||
|
|
# 参考链接 https://blog.csdn.net/weixin_43718675/article/details/103497930
|
|||
|
|
ori_geo_tif[np.isnan(ori_geo_tif)] = -1
|
|||
|
|
lon_data = ori_geo_tif[0, :, :].reshape(-1)
|
|||
|
|
lat_data = ori_geo_tif[1, :, :].reshape(-1)
|
|||
|
|
idx = np.where(lat_data != -1)
|
|||
|
|
lat_data = lat_data[idx]
|
|||
|
|
lon_data = lon_data[idx]
|
|||
|
|
idx = np.where(lon_data != -1)
|
|||
|
|
|
|||
|
|
lat_data = lat_data[idx]
|
|||
|
|
lon_data = lon_data[idx]
|
|||
|
|
# ###########################################
|
|||
|
|
result = self.interp2d_station_to_grid(lon_data, lat_data, produc_arr,
|
|||
|
|
[self._min_lat, self._max_lat, self._min_lon, self._max_lon],
|
|||
|
|
det_grid=pixel_delta, method=method)
|
|||
|
|
return result
|
|||
|
|
|
|||
|
|
def l1a_2_geo(self, ori_geo_path, l1a_produc_path, geo_produc_path, method='linear'):
|
|||
|
|
ori_geo_tif = ImageHandle.ImageHandler.get_data(ori_geo_path)
|
|||
|
|
# l1a_produc = ImageHandle.ImageHandler.get_data(l1a_produc_path)
|
|||
|
|
l1a_produc = ImageHandle.ImageHandler.get_band_array(l1a_produc_path, 1)
|
|||
|
|
pixel_delta_y = (self._max_lat - self._min_lat) / (self._end_r - self._begin_r) # 0.001
|
|||
|
|
pixel_delta_x = (self._max_lon - self._min_lon) / (self._end_c - self._begin_c)
|
|||
|
|
|
|||
|
|
lon_data = ori_geo_tif[0, :, :].reshape(-1)
|
|||
|
|
lat_data = ori_geo_tif[1, :, :].reshape(-1)
|
|||
|
|
l1a_produc = l1a_produc.reshape(-1)
|
|||
|
|
idx = np.logical_not(np.isnan(lon_data))
|
|||
|
|
lat_data = lat_data[idx]
|
|||
|
|
lon_data = lon_data[idx]
|
|||
|
|
l1a_produc = l1a_produc[idx]
|
|||
|
|
idx = np.logical_not(np.isnan(lat_data))
|
|||
|
|
lat_data = lat_data[idx]
|
|||
|
|
lon_data = lon_data[idx]
|
|||
|
|
l1a_produc = l1a_produc[idx]
|
|||
|
|
|
|||
|
|
gt = [self._min_lon, pixel_delta_x, 0.0,
|
|||
|
|
self._max_lat, 0.0, -pixel_delta_y]
|
|||
|
|
[lat_min, lat_max, lon_min, lon_max] = [self._min_lat, self._max_lat, self._min_lon, self._max_lon]
|
|||
|
|
lat_count = int((lat_max - lat_min) / pixel_delta_y + 1) # y
|
|||
|
|
lon_count = int((lon_max - lon_min) / pixel_delta_x + 1) # x
|
|||
|
|
|
|||
|
|
# 获取地理坐标系统信息，用于选取需要的地理坐标系统
|
|||
|
|
srs = osr.SpatialReference()
|
|||
|
|
srs.ImportFromEPSG(4326) # 定义输出的坐标系为"WGS 84"
|
|||
|
|
proj = srs.ExportToWkt()
|
|||
|
|
|
|||
|
|
projection = srs.ExportToPROJJSON()
|
|||
|
|
# （lower_left_x、lower_left_y、upper_right_x、upper_right_y）
|
|||
|
|
target_def = AreaDefinition("id1", "WGS84", "proj_id", projection,
|
|||
|
|
lon_count, lat_count, [lon_min, lat_min, lon_max, lat_max])
|
|||
|
|
lon_data = lon_data.reshape(-1, 1)
|
|||
|
|
lat_data = lat_data.reshape(-1, 1)
|
|||
|
|
l1a_produc = l1a_produc.reshape(-1, 1)
|
|||
|
|
source_def = geometry.SwathDefinition(lons=lon_data, lats=lat_data)
|
|||
|
|
lalo_step = [pixel_delta_x, -pixel_delta_y]
|
|||
|
|
radius = TransImgL1A.get_radius_of_influence(lalo_step, src_meta='radar2geo')
|
|||
|
|
geo_produc = pr.bilinear.resample_bilinear(l1a_produc, source_def, target_def,
|
|||
|
|
radius=radius, neighbours=32,
|
|||
|
|
nprocs=8, fill_value=np.nan,
|
|||
|
|
epsilon=0)
|
|||
|
|
|
|||
|
|
ImageHandle.ImageHandler.write_img(geo_produc_path, proj, gt, geo_produc, np.nan)
|
|||
|
|
|
|||
|
|
def l1a_2_geo_int(self, ori_geo_path, l1a_produc_path, geo_produc_path, method='nearest'):
|
|||
|
|
ori_geo_tif = ImageHandle.ImageHandler.get_data(ori_geo_path)
|
|||
|
|
# l1a_produc = ImageHandle.ImageHandler.get_data(l1a_produc_path)
|
|||
|
|
l1a_produc = ImageHandle.ImageHandler.get_band_array(l1a_produc_path, 1)
|
|||
|
|
pixel_delta_y = (self._max_lat - self._min_lat) / (self._end_r - self._begin_r) # 0.001
|
|||
|
|
pixel_delta_x = (self._max_lon - self._min_lon) / (self._end_c - self._begin_c)
|
|||
|
|
|
|||
|
|
lon_data = ori_geo_tif[0, :, :].reshape(-1)
|
|||
|
|
lat_data = ori_geo_tif[1, :, :].reshape(-1)
|
|||
|
|
l1a_produc = l1a_produc.reshape(-1)
|
|||
|
|
idx = np.logical_not(np.isnan(lon_data))
|
|||
|
|
lat_data = lat_data[idx]
|
|||
|
|
lon_data = lon_data[idx]
|
|||
|
|
l1a_produc = l1a_produc[idx]
|
|||
|
|
idx = np.logical_not(np.isnan(lat_data))
|
|||
|
|
lat_data = lat_data[idx]
|
|||
|
|
lon_data = lon_data[idx]
|
|||
|
|
l1a_produc = l1a_produc[idx]
|
|||
|
|
|
|||
|
|
gt = [self._min_lon, pixel_delta_x, 0.0,
|
|||
|
|
self._max_lat, 0.0, -pixel_delta_y]
|
|||
|
|
[lat_min, lat_max, lon_min, lon_max] = [self._min_lat, self._max_lat, self._min_lon, self._max_lon]
|
|||
|
|
lat_count = int((lat_max - lat_min) / pixel_delta_y + 1) # y
|
|||
|
|
lon_count = int((lon_max - lon_min) / pixel_delta_x + 1) # x
|
|||
|
|
|
|||
|
|
# 获取地理坐标系统信息，用于选取需要的地理坐标系统
|
|||
|
|
srs = osr.SpatialReference()
|
|||
|
|
srs.ImportFromEPSG(4326) # 定义输出的坐标系为"WGS 84"
|
|||
|
|
proj = srs.ExportToWkt()
|
|||
|
|
|
|||
|
|
projection = srs.ExportToPROJJSON()
|
|||
|
|
# （lower_left_x、lower_left_y、upper_right_x、upper_right_y）
|
|||
|
|
target_def = AreaDefinition("id1", "WGS84", "proj_id", projection,
|
|||
|
|
lon_count, lat_count, [lon_min, lat_min, lon_max, lat_max])
|
|||
|
|
lon_data = lon_data.reshape(-1, 1)
|
|||
|
|
lat_data = lat_data.reshape(-1, 1)
|
|||
|
|
l1a_produc = l1a_produc.reshape(-1, 1)
|
|||
|
|
source_def = geometry.SwathDefinition(lons=lon_data, lats=lat_data)
|
|||
|
|
lalo_step = [pixel_delta_x, -pixel_delta_y]
|
|||
|
|
radius = TransImgL1A.get_radius_of_influence(lalo_step, src_meta='radar2geo')
|
|||
|
|
if method == 'linear':
|
|||
|
|
geo_produc = pr.bilinear.resample_bilinear(l1a_produc, source_def, target_def,
|
|||
|
|
radius=radius, neighbours=32,
|
|||
|
|
nprocs=8, fill_value=0,
|
|||
|
|
epsilon=0)
|
|||
|
|
elif method == 'nearest':
|
|||
|
|
geo_produc = pr.kd_tree.resample_nearest(source_def, l1a_produc, target_def, epsilon=0,
|
|||
|
|
radius_of_influence=50000,
|
|||
|
|
fill_value=0, nprocs=8
|
|||
|
|
)
|
|||
|
|
geo_produc = geo_produc[:,:,0]
|
|||
|
|
ImageHandle.ImageHandler.write_img(geo_produc_path, proj, gt, geo_produc)
|
|||
|
|
|
|||
|
|
@property
|
|||
|
|
def mask(self):
|
|||
|
|
return self._mask
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
# ori_sim_path = r"I:\坐标转换\坐标转换接口\L1A数据（l1a_img_path数据）\RPC_ori_sim.tif"
|
|||
|
|
# roi_Extend = [[120.53, 31.5], [120.61, 31.5], [120.61, 31.45], [120.53, 31.45]]
|
|||
|
|
# conver_path = r"I:\坐标转换\坐标转换接口\裁剪后辅助数据（geo_img_path数据）\Covering_cut.tif"
|
|||
|
|
# ndvi_path = r"I:\坐标转换\坐标转换接口\裁剪后辅助数据（geo_img_path数据）\NDVI_cut.tif"
|
|||
|
|
# out_path = r"I:\坐标转换\SAR2GEO\test"
|
|||
|
|
#
|
|||
|
|
# tr = TransImgL1A(ori_sim_path,roi_Extend)
|
|||
|
|
# tr.l1a_2_geo("I:/cut.tif", "I:/salinity.tif", "I:/salinity_geo2.tif")
|
|||
|
|
ori_sim = r'F:\20230605\ortho\RD_ori_sim.tif'
|
|||
|
|
product_tif = r'D:\micro\WorkSpace\soil_test\proces\oh2004tif\oh2004_mv.tif'
|
|||
|
|
result = r'D:\micro\WorkSpace\soil_test\proces\oh2004tif\soil_geo.tif'
|
|||
|
|
method = 'linear'
|
|||
|
|
"""
|
|||
|
|
31.14;31.50;120.34;120.75
|
|||
|
|
"""
|
|||
|
|
# roi_Extend = [[102.12, 33.879], [102.327, 33.879], [102.327, 33.66], [102.12, 31.45]]
|
|||
|
|
ori_sim_data = ImageHandle.ImageHandler.get_data(ori_sim)
|
|||
|
|
lon = ori_sim_data[0,:,:]
|
|||
|
|
lat = ori_sim_data[1,:,:]
|
|||
|
|
print(np.nanmin(lon))
|
|||
|
|
print(np.nanmax(lon))
|
|||
|
|
print(np.nanmin(lat))
|
|||
|
|
print(np.nanmax(lat))
|
|||
|
|
roi_Extend = [[121.4627, 41.255276], [121.91531, 41.255276], [121.91531, 40.86639], [121.4627, 40.86639]]
|
|||
|
|
# roi_Extend = [[108.51960117899473, 38.192443138079895], [109.62308480328566, 38.192443138079895], [109.62308480328566, 37.69300142375064], [108.51960117899473, 37.69300142375064]]
|
|||
|
|
|
|||
|
|
tr = TransImgL1A(ori_sim, roi_Extend)
|
|||
|
|
tr.l1a_2_geo_int(ori_sim, product_tif, result, method)
|
|||
|
|
|
|||
|
|
pass
|
|||
|
|
"""
|
|||
|
|
import numpy as np
|
|||
|
|
from pyresample import kd_tree, geometry
|
|||
|
|
area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
|
|||
|
|
{'a': '6378144.0', 'b': '6356759.0',
|
|||
|
|
'lat_0': '50.00', 'lat_ts': '50.00',
|
|||
|
|
'lon_0': '8.00', 'proj': 'stere'},
|
|||
|
|
800, 800,
|
|||
|
|
[-1370912.72, -909968.64,
|
|||
|
|
1029087.28, 1490031.36])
|
|||
|
|
data = np.fromfunction(lambda y, x: y*x, (50, 10))
|
|||
|
|
lons = np.fromfunction(lambda y, x: 3 + x, (50, 10))
|
|||
|
|
lats = np.fromfunction(lambda y, x: 75 - y, (50, 10))
|
|||
|
|
swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
|
|||
|
|
result = kd_tree.resample_nearest(swath_def, data,area_def, radius_of_influence=50000, epsilon=0.5)
|
|||
|
|
"""
|