462 lines
20 KiB
Python
462 lines
20 KiB
Python
import numpy as np
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import os
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from osgeo import gdal
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from util import *
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from type_define import *
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import math
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from pyproj import CRS
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from pyproj import Transformer
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import argparse
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import json
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def get_coor_base_lang_lat(img_path, water_mask, severe_glint, csv_pos, coor_type, radius, point_pos_strategy=PointPosStrategy.nearest_single, img_type=ImgType.ref):
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"""
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此函数用于获取影像（img_path）在经纬度坐标（csv_pos）处的值，并输出到文件（out_txt）；
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:param img_path: data ignore value应该为0；hdr不应该带bil等后缀
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:param img_type: 枚举ImgType
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:param csv_pos:
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:param coor_type: 枚举CoorType
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:param radius: 单位为米
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:param point_pos_strategy: 枚举PointPosStrategy
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:param severe_glint: 单波段图像，1代表耀斑严重的区域
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:return:
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"""
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dataset = gdal.Open(img_path)
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im_width = dataset.RasterXSize # 栅格矩阵的列数
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im_height = dataset.RasterYSize # 栅格矩阵的行数
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num_bands = dataset.RasterCount # 栅格矩阵的波段数
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geotransform = dataset.GetGeoTransform() # 仿射矩阵
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im_proj = dataset.GetProjection() # 地图投影信息
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inv_geotransform_input = gdal.InvGeoTransform(geotransform)
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band_number_used = 1 # 找绿波段干啥？
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if img_type == ImgType.ref:
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green_wave = 560 # 绿光信号最强
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band_number_used = find_band_number(green_wave, img_path)
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elif img_type == ImgType.content:
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band_number_used = 0
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position_content = np.loadtxt(csv_pos, delimiter="\t")
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new_columns_num = 4 # + num_bands
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new_columns = np.zeros((position_content.shape[0], new_columns_num))
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position_content = np.hstack((position_content, new_columns))
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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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geotransform_severe_glint = dataset_severe_glint.GetGeoTransform() # 仿射矩阵
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data_severe_glint = dataset_severe_glint.GetRasterBand(1).ReadAsArray()
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inv_geotransform_severe_glint = gdal.InvGeoTransform(geotransform_severe_glint)
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if coor_type == CoorType.latlong: # gps
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epsg_code = 32600 + math.floor(position_content[0, 0] / 6) + 31
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crs = CRS.from_epsg(epsg_code)
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projTransformer = Transformer.from_crs(crs.geodetic_crs, crs)
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for i in range(position_content.shape[0]):
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if coor_type == CoorType.latlong: # gps
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utm_cor = projTransformer.transform(position_content[i, 1], position_content[i, 0])
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elif coor_type == CoorType.utm: # utm
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utm_cor = (position_content[i, 0], position_content[i, 1])
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img_coor = gdal.ApplyGeoTransform(inv_geotransform_input, utm_cor[0], utm_cor[1]) # (x,y)
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img_coor = [int(i) for i in img_coor]
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radius_pixel_data = math.ceil(radius / geotransform[1] / 2)
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data_local = dataset.GetRasterBand(band_number_used + 1).ReadAsArray(img_coor[0] - radius_pixel_data,
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img_coor[1] - radius_pixel_data,
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2 * radius_pixel_data + 1,
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2 * radius_pixel_data + 1)
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water_mask_local = dataset_water_mask.GetRasterBand(1).ReadAsArray(img_coor[0] - radius_pixel_data,
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img_coor[1] - radius_pixel_data,
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2 * radius_pixel_data + 1,
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2 * radius_pixel_data + 1)
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# if img_type == ImgType.content:
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# data_local = dataset.ReadAsArray(img_coor[0] - radius_pixel_data, img_coor[1] - radius_pixel_data,
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# 2 * radius_pixel_data + 1, 2 * radius_pixel_data + 1)
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# elif img_type == ImgType.ref:
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# data_local = dataset.GetRasterBand(band_number_used + 1).ReadAsArray(img_coor[0] - radius_pixel_data,
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# img_coor[1] - radius_pixel_data,
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# 2 * radius_pixel_data + 1,
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# 2 * radius_pixel_data + 1)
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img_coor_severe_glint = gdal.ApplyGeoTransform(inv_geotransform_severe_glint, utm_cor[0], utm_cor[1])
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img_coor_severe_glint = [int(i) for i in img_coor_severe_glint]
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if geotransform[1] == geotransform_severe_glint[1]:
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radius_severe_glint = math.ceil(radius / geotransform_severe_glint[1] / 2) #
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severe_glint_local = data_severe_glint[img_coor_severe_glint[1] - radius_severe_glint:img_coor_severe_glint[1] + radius_severe_glint + 1,
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img_coor_severe_glint[0] - radius_severe_glint:img_coor_severe_glint[0] + radius_severe_glint + 1]
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else: # 当分辨率不一致时，还没完成????????????????????????????????????????????????????????
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severe_glint_local = np.zeros(data_local.shape)
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for x in range(2 * radius_pixel_data + 1):
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for y in range(2 * radius_pixel_data + 1):
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img_coor_tmp = gdal.ApplyGeoTransform(geotransform, utm_cor[0], utm_cor[1])
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img_coor_tmp2 = gdal.ApplyGeoTransform(inv_geotransform_severe_glint, img_coor_tmp[0], img_coor_tmp[1])
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severe_glint_local[y, x] = data_severe_glint[img_coor_tmp[1], img_coor_tmp[0]]
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invalid_value = 0
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data_local[severe_glint_local == 1] = invalid_value
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data_local[water_mask_local == 0] = invalid_value
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# 根据PointPosStrategy确定使用哪些位置的数据
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if point_pos_strategy == PointPosStrategy.nearest_single:
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coor_y_local, coor_x_local = getnearest(data_local, invalid_value)
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if coor_x_local is None:
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continue
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coor_y = coor_y_local + img_coor[1] - radius_pixel_data
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coor_x = coor_x_local + img_coor[0] - radius_pixel_data
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utm_x, utm_y = gdal.ApplyGeoTransform(geotransform, coor_x, coor_y)
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position_content[i, position_content.shape[1] - new_columns_num] = utm_x
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position_content[i, position_content.shape[1] - new_columns_num + 1] = utm_y
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position_content[i, position_content.shape[1] - new_columns_num + 2] = int(coor_x)
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position_content[i, position_content.shape[1] - new_columns_num + 3] = int(coor_y)
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# position_content[i, position_content.shape[1] - new_columns_num + 4:position_content.shape[1]] = \
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# dataset.ReadAsArray(int(coor_x), int(coor_y), 1, 1).reshape(num_bands)
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a = 1
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elif point_pos_strategy == PointPosStrategy.four_quadrant: # 还没完成????????????????????????????????????????????????????????
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a = 1
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a = 1
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rows_to_keep = np.where(position_content[:, position_content.shape[1] - new_columns_num + 3] != 0)
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position_content2 = position_content[rows_to_keep]
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position_content3 = position_content2[~np.isnan(position_content2).any(axis=1)]
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# np.savetxt(out_txt, position_content3)
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del dataset, dataset_severe_glint, dataset_water_mask
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return position_content3
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def fit(x1, x2, y):
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A = np.column_stack((x1, x2, np.ones((x2.shape[0], 1))))
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coefficients, _, _, _ = np.linalg.lstsq(A, y, rcond=None)
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return coefficients
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def accuracy_evaluation(x1, x2, y_real, coefficients):
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A = np.column_stack((x1, x2, np.ones((x2.shape[0], 1))))
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y_pred = A.dot(coefficients)
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accuracy = np.absolute((y_real - y_pred) / y_real * 100)
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return accuracy
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def accuracy_evaluation_tss(x1, x2, y_real, coefficients):
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A = np.column_stack((x1, x2, np.ones((x2.shape[0], 1))))
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y = A.dot(coefficients)
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y_pred = np.exp(y)
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accuracy = np.absolute((y_real - y_pred) / y_real * 100)
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return accuracy
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def get_x_in_coor(coor, *args):
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new_columns_counter = len(args)
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new_columns = np.zeros((coor.shape[0], new_columns_counter))
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coor_extend = np.hstack((coor, new_columns))
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for i in range(coor.shape[0]):
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for j in range(new_columns_counter):
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coor_extend[i, coor_extend.shape[1] - (new_columns_counter - j)] = args[j][
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int(coor_extend[i, coor_extend.shape[1] - new_columns_counter - 1]),
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int(coor_extend[i, coor_extend.shape[1] - new_columns_counter - 2])]
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return coor_extend
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def write_model_info(model_type, coefficients, accuracy, long, lat, outpath):
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# 将 NumPy 数组转换为列表
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np_dict = {
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'model_type': model_type,
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'model_info': coefficients.tolist(),
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'accuracy': accuracy.tolist(),
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'long': long.tolist(),
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'lat': lat.tolist()
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}
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# 将字典写入 JSON 文件，使用 indent 参数进行格式化（每一级缩进4个空格）
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with open(outpath, 'w') as f:
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json.dump(np_dict, f, indent=4)
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def chl_a(img_path, coor, outpath_coeff, window=5): # 叶绿素
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wave1 = 651
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band_651 = average_bands(wave1 - window, wave1 + window, img_path)
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wave2 = 707
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band_707 = average_bands(wave2 - window, wave2 + window, img_path)
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wave3 = 670
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band_670 = average_bands(wave3 - window, wave3 + window, img_path)
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x = (band_651 - band_707) / (band_707 - band_670)
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coor_extend = get_x_in_coor(coor, x)
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# 修正模型参数并输出
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x = coor_extend[:, -1]
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y_real = coor_extend[:, 2]
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coefficients = np.polyfit(list(x), list(y_real), 1)
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y_pred = np.polyval(coefficients, list(x))
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accuracy = np.absolute((y_real - y_pred) / y_real * 100)
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write_model_info("chl-a", coefficients, accuracy, coor_extend[:, 0], coor_extend[:, 1], outpath_coeff)
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model_type, model_info, accuracy_ = load_numpy_dict_from_json(outpath_coeff)
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return coefficients
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def nh3(img_path, coor, outpath_coeff, window=5): # 氨氮
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wave1 = 600
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band_600 = average_bands(wave1 - window, wave1 + window, img_path)
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wave2 = 500
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band_500 = average_bands(wave2 - window, wave2 + window, img_path)
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wave3 = 850
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band_850 = average_bands(wave3 - window, wave3 + window, img_path)
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x13 = np.log(band_500 / band_850)
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x23 = np.exp(band_600 / band_500)
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coor_extend = get_x_in_coor(coor, x13, x23)
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# 修正模型参数并输出
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x1 = coor_extend[:, -2]
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x2 = coor_extend[:, -1]
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y_real = coor_extend[:, 2]
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coefficients = fit(x1, x2, y_real)
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accuracy = accuracy_evaluation(x1, x2, y_real, coefficients)
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write_model_info("nh3", coefficients, accuracy, coor_extend[:, 0], coor_extend[:, 1], outpath_coeff)
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model_type, model_info, accuracy_ = load_numpy_dict_from_json(outpath_coeff)
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return coefficients
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def mno4(img_path, coor, outpath_coeff, window=5): # 高猛酸盐
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wave1 = 500
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band_500 = average_bands(wave1 - window, wave1 + window, img_path)
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wave2 = 440
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band_440 = average_bands(wave2 - window, wave2 + window, img_path)
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wave3 = 610
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band_610 = average_bands(wave3 - window, wave3 + window, img_path)
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wave4 = 800
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band_800 = average_bands(wave4 - window, wave4 + window, img_path)
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x3 = band_500 / band_440
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x6 = band_610 / band_800
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coor_extend = get_x_in_coor(coor, x3, x6)
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# 修正模型参数并输出
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x1 = coor_extend[:, -2]
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x2 = coor_extend[:, -1]
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y_real = coor_extend[:, 2]
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coefficients = fit(x1, x2, y_real)
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accuracy = accuracy_evaluation(x1, x2, y_real, coefficients)
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write_model_info("mno4", coefficients, accuracy, coor_extend[:, 0], coor_extend[:, 1], outpath_coeff)
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model_type, model_info, accuracy_ = load_numpy_dict_from_json(outpath_coeff)
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return coefficients
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def tn(img_path, coor, outpath_coeff, window=5): # 总氮
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wave1 = 600
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band_600 = average_bands(wave1 - window, wave1 + window, img_path)
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wave2 = 500
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band_500 = average_bands(wave2 - window, wave2 + window, img_path)
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wave3 = 850
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band_850 = average_bands(wave3 - window, wave3 + window, img_path)
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x13 = np.log(band_500 / band_850)
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x23 = np.exp(band_600 / band_500)
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coor_extend = get_x_in_coor(coor, x13, x23)
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# 修正模型参数并输出
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x1 = coor_extend[:, -2]
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x2 = coor_extend[:, -1]
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y_real = coor_extend[:, 2]
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coefficients = fit(x1, x2, y_real)
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accuracy = accuracy_evaluation(x1, x2, y_real, coefficients)
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write_model_info("tn", coefficients, accuracy, coor_extend[:, 0], coor_extend[:, 1], outpath_coeff)
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model_type, model_info, accuracy_ = load_numpy_dict_from_json(outpath_coeff)
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return coefficients
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def tp(img_path, coor, outpath_coeff, window=5): # 总磷
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wave1 = 600
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band_600 = average_bands(wave1 - window, wave1 + window, img_path)
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wave2 = 500
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band_500 = average_bands(wave2 - window, wave2 + window, img_path)
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wave3 = 850
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band_850 = average_bands(wave3 - window, wave3 + window, img_path)
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x13 = np.log(band_500 / band_850)
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x23 = np.exp(band_600 / band_500)
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coor_extend = get_x_in_coor(coor, x13, x23)
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# 修正模型参数并输出
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x1 = coor_extend[:, -2]
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x2 = coor_extend[:, -1]
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y_real = coor_extend[:, 2]
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coefficients = fit(x1, x2, y_real)
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accuracy = accuracy_evaluation(x1, x2, y_real, coefficients)
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write_model_info("tp", coefficients, accuracy, coor_extend[:, 0], coor_extend[:, 1], outpath_coeff)
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model_type, model_info, accuracy_ = load_numpy_dict_from_json(outpath_coeff)
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return coefficients
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def tss(img_path, coor, outpath_coeff, window=5): # 总悬浮物？？？？？？？？？？？？？？？？？？
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wave1 = 555
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band_555 = average_bands(wave1 - window, wave1 + window, img_path)
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wave2 = 670
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band_670 = average_bands(wave2 - window, wave2 + window, img_path)
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wave3 = 490
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band_490 = average_bands(wave3 - window, wave3 + window, img_path)
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x1 = band_555 + band_670
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x2 = band_490 / band_555
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coor_extend = get_x_in_coor(coor, x1, x2)
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# 修正模型参数并输出
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x1 = coor_extend[:, -2]
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x2 = coor_extend[:, -1]
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y_real = coor_extend[:, 2]
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y = np.log(y_real)
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coefficients = fit(x1, x2, y)
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accuracy = accuracy_evaluation_tss(x1, x2, y_real, coefficients)
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write_model_info("tss", coefficients, accuracy, coor_extend[:, 0], coor_extend[:, 1], outpath_coeff)
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model_type, model_info, accuracy_ = load_numpy_dict_from_json(outpath_coeff)
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return coefficients
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def main():
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parser = argparse.ArgumentParser(description="此程序用于通过实测数据修正模型参数。")
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# parser.add_argument("--global_arg", type=str, help="A global argument for all modes", required=True)
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# 创建子命令解析器
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subparsers = parser.add_subparsers(dest="algorithm", required=True, help="Choose a mode")
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chl_a_ = subparsers.add_parser("chl_a", help="叶绿素")
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chl_a_.add_argument('-i1', '--img', type=str, required=True, help='输入影像文件的路径')
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chl_a_.add_argument('-i2', '--water_mask', type=str, required=True, help='输入水域掩膜文件的路径')
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chl_a_.add_argument('-i3', '--severe_glint', type=str, required=True, help='输入耀斑严重文件的路径')
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chl_a_.add_argument('-i4', '--measured_data', type=str, required=True, help='输入实测含量数据的路径')
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chl_a_.add_argument('-i5', '--radius', type=float, default=2.0, help='输入实测坐标半径')
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chl_a_.add_argument('-o', '--model_info_outpath', required=True, type=str, help='输出模型信息文件的路径')
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chl_a_.set_defaults(func=chl_a)
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nh3_ = subparsers.add_parser("nh3", help="氨氮")
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nh3_.add_argument('-i1', '--img', type=str, required=True, help='输入影像文件的路径')
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nh3_.add_argument('-i2', '--water_mask', type=str, required=True, help='输入水域掩膜文件的路径')
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||
nh3_.add_argument('-i3', '--severe_glint', type=str, required=True, help='输入耀斑严重文件的路径')
|
||
nh3_.add_argument('-i4', '--measured_data', type=str, required=True, help='输入实测含量数据的路径')
|
||
nh3_.add_argument('-i5', '--radius', type=float, default=2.0, help='输入实测坐标半径')
|
||
nh3_.add_argument('-o', '--model_info_outpath', required=True, type=str, help='输出模型信息文件的路径')
|
||
nh3_.set_defaults(func=nh3)
|
||
|
||
mno4_ = subparsers.add_parser("mno4", help="高猛酸盐")
|
||
mno4_.add_argument('-i1', '--img', type=str, required=True, help='输入影像文件的路径')
|
||
mno4_.add_argument('-i2', '--water_mask', type=str, required=True, help='输入水域掩膜文件的路径')
|
||
mno4_.add_argument('-i3', '--severe_glint', type=str, required=True, help='输入耀斑严重文件的路径')
|
||
mno4_.add_argument('-i4', '--measured_data', type=str, required=True, help='输入实测含量数据的路径')
|
||
mno4_.add_argument('-i5', '--radius', type=float, default=2.0, help='输入实测坐标半径')
|
||
mno4_.add_argument('-o', '--model_info_outpath', required=True, type=str, help='输出模型信息文件的路径')
|
||
mno4_.set_defaults(func=mno4)
|
||
|
||
tn_ = subparsers.add_parser("tn", help="总氮")
|
||
tn_.add_argument('-i1', '--img', type=str, required=True, help='输入影像文件的路径')
|
||
tn_.add_argument('-i2', '--water_mask', type=str, required=True, help='输入水域掩膜文件的路径')
|
||
tn_.add_argument('-i3', '--severe_glint', type=str, required=True, help='输入耀斑严重文件的路径')
|
||
tn_.add_argument('-i4', '--measured_data', type=str, required=True, help='输入实测含量数据的路径')
|
||
tn_.add_argument('-i5', '--radius', type=float, default=2.0, help='输入实测坐标半径')
|
||
tn_.add_argument('-o', '--model_info_outpath', required=True, type=str, help='输出模型信息文件的路径')
|
||
tn_.set_defaults(func=tn)
|
||
|
||
tp_ = subparsers.add_parser("tp", help="总磷")
|
||
tp_.add_argument('-i1', '--img', type=str, required=True, help='输入影像文件的路径')
|
||
tp_.add_argument('-i2', '--water_mask', type=str, required=True, help='输入水域掩膜文件的路径')
|
||
tp_.add_argument('-i3', '--severe_glint', type=str, required=True, help='输入耀斑严重文件的路径')
|
||
tp_.add_argument('-i4', '--measured_data', type=str, required=True, help='输入实测含量数据的路径')
|
||
tp_.add_argument('-i5', '--radius', type=float, default=2.0, help='输入实测坐标半径')
|
||
tp_.add_argument('-o', '--model_info_outpath', required=True, type=str, help='输出模型信息文件的路径')
|
||
tp_.set_defaults(func=tp)
|
||
|
||
tss_ = subparsers.add_parser("tss", help="总悬浮物")
|
||
tss_.add_argument('-i1', '--img', type=str, required=True, help='输入影像文件的路径')
|
||
tss_.add_argument('-i2', '--water_mask', type=str, required=True, help='输入水域掩膜文件的路径')
|
||
tss_.add_argument('-i3', '--severe_glint', type=str, required=True, help='输入耀斑严重文件的路径')
|
||
tss_.add_argument('-i4', '--measured_data', type=str, required=True, help='输入实测含量数据的路径')
|
||
tss_.add_argument('-i5', '--radius', type=float, default=2.0, help='输入实测坐标半径')
|
||
tss_.add_argument('-o', '--model_info_outpath', required=True, type=str, help='输出模型信息文件的路径')
|
||
tss_.set_defaults(func=tss)
|
||
|
||
# 解析参数
|
||
args = parser.parse_args()
|
||
tmp = get_coor_base_lang_lat(args.img, args.water_mask, args.severe_glint, args.measured_data, CoorType.latlong,
|
||
args.radius)
|
||
if args.algorithm == "chl_a":
|
||
args.func(args.img, tmp, args.model_info_outpath)
|
||
elif args.algorithm == "nh3":
|
||
args.func(args.img, tmp, args.model_info_outpath)
|
||
elif args.algorithm == "mno4":
|
||
args.func(args.img, tmp, args.model_info_outpath)
|
||
elif args.algorithm == "tn":
|
||
args.func(args.img, tmp, args.model_info_outpath)
|
||
elif args.algorithm == "tp":
|
||
args.func(args.img, tmp, args.model_info_outpath)
|
||
elif args.algorithm == "tss":
|
||
args.func(args.img, tmp, args.model_info_outpath)
|
||
|
||
|
||
# Press the green button in the gutter to run the script.
|
||
if __name__ == '__main__':
|
||
main()
|