86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
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import os
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import glob
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import numpy as np
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import struct
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from PIL import Image
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from tool.algorithm.ml.machineLearning import MachineLeaning as ml
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def read_bin_to_img(bin_path):
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"""
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读取bin格式二进制数据，输出为矩阵
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:param bin_path : bin文件的路径，包含.bin,.config
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:return : 矩阵信息
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"""
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(bin_dir, bin_name) = os.path.split(bin_path)
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config_path = os.path.join(bin_dir, 'config.txt')
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config = open(config_path, 'r').read().split('\n', -1)
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rows = int(config[1])
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cols = int(config[4])
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bin_file = open(bin_path, 'rb') # 打开二进制文件
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size = os.path.getsize(bin_path) # 获得文件大小
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if size < rows * cols * 4:
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raise Exception(
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'bin size less than rows*cols*4! size:',
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size,
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'byte, rows:',
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rows,
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'cols:',
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cols)
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img = np.zeros([rows, cols], dtype=np.float32)
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for row in range(rows):
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data = bin_file.read(4 * cols) # 每次读取一行的二进制数据
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row_data = struct.unpack('f' * cols, data) # 转为一行float数据
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img[row, :] = row_data
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bin_file.close()
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return img
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def write_bin_to_tif(out_tif_dir, bin_dir):
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"""
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读取H-A-Alpha分解二进制数据，输出为矩阵格式的字典
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:param out_tif_dir : tif的输出路径
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:param bin_dir : 二进制数据的目录,包含.bin,.config
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:return out_tif_path: 生成tif的路径字典
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"""
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bin_paths = list(glob.glob(os.path.join(bin_dir, '*.bin')))
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out_tif_path = {}
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for in_path in bin_paths:
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name = os.path.split(in_path)[1].split('.')[0]
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out_path = os.path.join(out_tif_dir, name + '.tif')
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out_tif_path.update({name: out_path})
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if os.path.exists(os.path.split(out_path)[0]) is False:
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os.makedirs(os.path.split(out_path)[0])
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img_array = read_bin_to_img(in_path)
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img_array[np.isnan(img_array)] = 0 # 异常值填充为0
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img_array = ml.standardization(img_array) # 数据标准化到[0,1]
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out_image = Image.fromarray(img_array)
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out_image.save(out_path)
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return out_tif_path
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def write_bin_to_tif_soil(out_tif_dir, bin_dir):
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"""
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读取H-A-Alpha分解二进制数据，输出为矩阵格式的字典
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:param out_tif_dir : tif的输出路径
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:param bin_dir : 二进制数据的目录,包含.bin,.config
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:return out_tif_path: 生成tif的路径字典
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"""
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bin_paths = list(glob.glob(os.path.join(bin_dir, '*.bin')))
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out_tif_path = {}
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for in_path in bin_paths:
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name = os.path.split(in_path)[1].split('.')[0]
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out_path = os.path.join(out_tif_dir, name + '.tif')
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out_tif_path.update({name: out_path})
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if os.path.exists(os.path.split(out_path)[0]) is False:
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os.makedirs(os.path.split(out_path)[0])
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img_array = read_bin_to_img(in_path)
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img_array[np.isnan(img_array)] = 0 # 异常值填充为0
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# img_array = ml.standardization(img_array) # 数据标准化到[0,1]
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out_image = Image.fromarray(img_array)
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out_image.save(out_path)
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return out_tif_path
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