microproduct/vegetationPhenology/VegetationPhenologyAuxData.py

# -*- coding: UTF-8 -*-
"""
@Project:SalinityMain.py
@File:AuxData.py
@Function: 读取指标物候辅助数据
@Contact:
@Author:SHJ
@Date:2021/10/21 19:25 
@Version:1.0.0
"""
import csv
import numpy as np
import mahotas
import logging
from tool.algorithm.algtools.CoordinateTransformation import lonlat2geo, geo2imagexy
from tool.algorithm.image.ImageHandle import ImageHandler
logger = logging.getLogger("mylog")


class PhenologyMeasCsv:

    def __init__(self, csv_path, preprocessed_paras):
        self.__csv_path = csv_path
        self.__preprocessed_paras = preprocessed_paras

    def api_read_phenology_measure(self):
        csv_data = self.__readcsv(self.__csv_path)
        return self.__trans_measuredata(csv_data)

    def class_list(self):
        """
        输出csv表中的前三列
        """
        reader = csv.reader(open(self.__csv_path, newline=''))
        class_list=[]
        type_id_name = {}
        for line_data in reader:
            class_list.append(line_data)    # class_list含有四列
        for data in class_list[1:]:
            type_id = data[2]
            type_name = data[3]

            if type_id not in type_id_name.keys():
                if type_id.strip() != "":
                    type_id_name.update({type_id: type_name})
        return type_id_name
        pass

    @staticmethod
    def __readcsv(csv_path):
        """
        读取csv表格数据
        :para csv_path: csv文件路径
        """
        reader = csv.reader(open(csv_path, newline=''))
        csv_list = []
        for line_data in reader:
            csv_list.append(line_data)
        return csv_list[1:]

    @staticmethod
    def readcsv_dic(csv_path):
        """
        读取csv表格数据
        :para csv_path: csv文件路径
        """
        reader = csv.reader(open(csv_path, newline=''))
        csv_list = []
        for line_data in reader:
            csv_list.append(line_data)
        keys = csv_list[0]
        datas = csv_list[1:]
        data_dic = {}
        for key ,n in  zip(keys,range(len(keys))):
            values = [data for data in datas[n]]
            data_dic.update({key: values})
        return data_dic


    def __trans_measuredata(self, meas_data):
        """
        获取多边形区域内所有的点，分为训练集数据和测试集数据
        :para meas_data: csv读取的实测数据
        """
        train_data = []
        test_data = []
        type_data = {}

        for data in meas_data:
            point_list = []
            data_use_type = data[0]

            sar_img_name = data[1]
            name = sar_img_name.rstrip('.tar.gz')
            dataset, rows, cols = self.__get_para_tif_inf(name)

            if data_use_type == 'train':
                phenology_id = int(data[2])
                phenology_name = data[3]
                if phenology_id not in type_data.keys():
                    type_data.update({phenology_id: phenology_name})

            else:
                phenology_id = -1

            pointList = self.__roiPolygonAnalysis(data[4])
            for points in pointList:
                poly = []
                for point in points:
                    lon = float(point[0])
                    lat = float(point[1])
                    projs = lonlat2geo(dataset, lon, lat)
                    coord = geo2imagexy(dataset, projs[1], projs[0])
                    row = round(coord[1])
                    col = round(coord[0])
                    if 0 <= row < rows and 0 <= col < cols:
                        poly.append([row, col])
                    else:
                        logger.warning("measure data: %s is beyond tif scope !", data)

                point_list = point_list + self.__render(poly)

            if data_use_type == 'train':
                train_data.append([name, phenology_id, point_list])
            elif data_use_type == 'test':
                test_data.append([name, phenology_id, point_list])

        type_map = []
        for n, id in zip(range(len(type_data)), type_data):
            type_map.append([n + 1, id, type_data[id]])

        return train_data, test_data, type_map

    @staticmethod
    def __render(poly):
        # https://www.cnpython.com/qa/51516
        """Return polygon as grid of points inside polygon.
        Input : poly (list of lists)
        Output : output (list of lists)
        """
        xs, ys = zip(*poly)
        minx, maxx = min(xs), max(xs)
        miny, maxy = min(ys), max(ys)

        newPoly = [(int(x - minx), int(y - miny)) for (x, y) in poly]

        X = maxx - minx + 1
        Y = maxy - miny + 1

        grid = np.zeros((X, Y), dtype=np.int8)
        mahotas.polygon.fill_polygon(newPoly, grid)

        return [(x + minx, y + miny) for (x, y) in zip(*np.nonzero(grid))]

    def __get_para_tif_inf(self, tif_name):
        """
        获取影像的信息
        :para tif_name: 影像名称
        """
        tif_path = self.__preprocessed_paras[tif_name + '_HH']
        ih = ImageHandler()
        dataset = ih.get_dataset(tif_path)
        rows = ih.get_img_height(tif_path)
        cols = ih.get_img_width(tif_path)
        return dataset, rows, cols

    @staticmethod
    def __roiPolygonAnalysis(roiStr):
        """
        将csv的POLY数据转为数组
        :para roiStr: poly数据
        :return pointList: 保存多边形的list
        """
        pointList = []
        strContent = roiStr.replace("POLYGON", "")
        # 解析轮廓字符串为二维数组
        bracketsList = []
        strTemp = ''
        strList = []
        for c in strContent:
            if c == '(':
                bracketsList.append(c)
                continue
            elif c == ')':
                if len(bracketsList) > 0:
                    bracketsList.pop(0)
                if len(strTemp) > 0:
                    strList.append(strTemp)
                strTemp = ''
            else:
                strTemp += c
        for item in strList:
            if len(item) == 0:
                continue
            pTempList = item.split(',')
            pList = []
            for row in pTempList:
                cells = row.split(' ')
                if len(cells) != 2:
                    continue
                point = []
                point.append(float(cells[0]))
                point.append(float(cells[1]))
                pList.append(point)
            pointList.append(pList)
        return pointList