import glob import multiprocessing import os import cv2 from VegetationPhenologyAuxData import PhenoloyMeasCsv_geo from tool.algorithm.image.ImageHandle import ImageHandler from tool.algorithm.ml.machineLearning import MachineLeaning as ml, MachineLeaning from tool.csv.csvHandle import csvHandle from tool.algorithm.block.blockprocess import BlockProcess as bp, BlockProcess import numpy as np from tool.algorithm.algtools.PreProcess import PreProcess as pp from tool.file.fileHandle import fileHandle file = fileHandle() csvh = csvHandle() MAX_TRAN_NUM = 100000 def predict_VP(clf, X_test_list, out_tif_name, workspace_processing_path, rows, cols): """ 预测数据 :param clf : svm模型 :return X_test_list: 分块测试集影像路径 """ ml = MachineLeaning() # 开启多进程处理 bp = BlockProcess() block_size = bp.get_block_size(rows, cols) name_d = out_tif_name + '_VTH' block_features_dir = X_test_list bp_cover_dir = os.path.join(workspace_processing_path, name_d, 'pre_result\\') # workspace_processing_path + out_tif_name + '\\' file.creat_dirs([bp_cover_dir]) processes_num = min([len(block_features_dir), multiprocessing.cpu_count() - 7]) pool = multiprocessing.Pool(processes=processes_num) for path, n in zip(block_features_dir, range(len(block_features_dir))): name = os.path.split(path)[1] band = ImageHandler.get_bands(path) if band == 1: features_array = np.zeros((1, block_size, block_size), dtype=float) feature_array = ImageHandler.get_data(path) features_array[0, :, :] = feature_array else: features_array = ImageHandler.get_data(path) X_test = np.reshape(features_array, (features_array.shape[0], features_array[0].size)).T suffix = '_' + name.split('_')[-4] + "_" + name.split('_')[-3] + "_" + name.split('_')[-2] + "_" + \ name.split('_')[-1] img_path = os.path.join(bp_cover_dir, name_d + suffix) # bp_cover_dir + out_tif_name + suffix row_begin = int(name.split('_')[-4]) col_begin = int(name.split('_')[-2]) pool.apply_async(ml.predict_blok, ( clf, X_test, block_size, block_size, img_path, row_begin, col_begin, len(block_features_dir), n)) # ml.predict_blok(clf, X_test, block_size, block_size, img_path, row_begin, col_begin, len(block_features_dir), n) pool.close() pool.join() del pool # 合并影像 data_dir = bp_cover_dir out_path = workspace_processing_path[0:-1] bp.combine(data_dir, cols, rows, out_path, file_type=['tif'], datetype='float32') # 添加地理信息 cover_path = os.path.join(out_path, name_d + ".tif") # workspace_processing_path + out_tif_name + ".tif" # bp.assign_spatial_reference_byfile(self.__ref_img_path, cover_path) return cover_path def featuresRoi(featurePath, roi): tif_dir = r"D:\BaiduNetdiskDownload\envi-result\cut_tif" blockDir = r'D:\BaiduNetdiskDownload\envi-result\block' pm = PhenoloyMeasCsv_geo(roi, featurePath, MAX_TRAN_NUM) train_data_list = pm.api_read_measure_by_name('0') train_data_dic = csvh.trans_landCover_list2dic(train_data_list) rows = ImageHandler.get_img_height(featurePath) cols = ImageHandler.get_img_width(featurePath) im_proj, im_geo, im_data = ImageHandler.read_img(featurePath) dim = ImageHandler.get_bands(featurePath) X_train = np.empty(shape=(0, dim)) Y_train = np.empty(shape=(0, 1)) ids = train_data_dic['ids'] positions = train_data_dic['positions'] for id, points in zip(ids, positions): # for data in train_data_list: if points == []: raise Exception('data is empty!') row, col = zip(*points) l = len(points) X = np.empty(shape=(l, dim)) for n in range(dim): feature_array = ImageHandler.get_band_array(featurePath, n+1) feature_array[np.isnan(feature_array)] = 0 # 异常值填充为0 feature_array[np.where(feature_array == -9999)] = 0 # 异常值填充为0 x = feature_array[row, col].T X[:, n] = x Y = np.full((l, 1), id) X_train = np.vstack((X_train, X)) Y_train = np.vstack((Y_train, Y)) Y_train = Y_train.T[0, :] clf = ml.trainRF(X_train, Y_train) bp().cut(tif_dir, blockDir, out_size=2048) X_test_list = list(glob.glob(os.path.join(blockDir, '*.tif'))) product_path = predict_VP(clf, X_test_list, 'geo', blockDir, rows, cols) proj, geo, cover_data = ImageHandler.read_img(product_path) # 形态学（闭运算）去roi区域噪点 cover_data = np.uint8(cover_data) kernel = np.ones((10, 10), np.uint8) cover_data = cv2.erode(cv2.dilate(cover_data, kernel), kernel) cover_data = np.int16(cover_data) for id, class_id in zip(train_data_dic['ids'], train_data_dic['class_ids']): cover_data[np.where(cover_data == id)] = class_id # cover_data[np.where(im_data[0, :, :] == -9999)] = -9999 cover_geo_path = os.path.join(blockDir, os.path.basename(product_path).split('.tif')[0] + '-VPtemp.tif') ImageHandler.write_img(cover_geo_path, im_proj, im_geo, cover_data, '-9999') if __name__ == '__main__': features = r"D:\micro\SWork\LandCover\Temporary\processing\features_geo\Freeman_Vol_geo.tif" roi = r"F:\xibei_LandCover\landCoverSamples.csv" featuresRoi(features, roi) # oytTil = r"D:\BaiduNetdiskDownload\54Features_land.tif" # outP = r"D:\BaiduNetdiskDownload\veg_landcover_re.tif" # # # im_proj, im_geotrans, im_arr = ImageHandler.read_img(features) # _, _, land = ImageHandler.read_img(outP) # # pp.resampling_by_scale(land, outP, features) # for i in range(im_arr.shape[0]): # im_arr[i, :, :][np.where(land==20)] = -9999 # im_arr[i, :, :][np.where(land==30)] = -9999 # im_arr[i, :, :][np.where(land==40)] = -9999 # im_arr[i, :, :][np.where(land==50)] = -9999 # im_arr[i, :, :][np.where(land==70)] = -9999 # im_arr[i, :, :][np.where(land==90)] = -9999 # im_arr[i, :, :][np.where(land==100)] = -9999 # ImageHandler.write_img(oytTil, im_proj, im_geotrans, im_arr, '-9999') # print(11)