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