Initial commit of WQ_GUI
This commit is contained in:
367
src/core/glint_removal/Goodman.py
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367
src/core/glint_removal/Goodman.py
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import numpy as np
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# import preprocessing
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try:
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from osgeo import gdal
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GDAL_AVAILABLE = True
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except ImportError:
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GDAL_AVAILABLE = False
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print("警告: GDAL未安装，将使用numpy处理模式")
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try:
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from tqdm import tqdm
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TQDM_AVAILABLE = True
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except ImportError:
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TQDM_AVAILABLE = False
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# 如果tqdm不可用，定义一个简单的包装器
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def tqdm(iterable, desc=None, total=None):
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return iterable
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class Goodman:
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def __init__(self, im_aligned, NIR_lower = 25, NIR_upper = 37, A = 0.000019, B = 0.1,
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use_gdal=True, chunk_size=None, water_mask=None, output_path=None):
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"""
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:param im_aligned (np.ndarray or str): band aligned and calibrated & corrected reflectance image
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可以是numpy数组或GDAL可读取的文件路径
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:param NIR_lower (int): band index which corresponds to 641.93nm, closest band to 640nm
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:param NIR_upper (int): band index which corresponds to 751.49nm, closest band to 750nm
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:param A (float): the values in Goodman et al's paper, using AVIRIS reflectance (rather than radiance) data
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:param B (float): the values in Goodman et al's paper, using AVIRIS reflectance (rather than radiance) data
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see Goodman et al, which corrects each pixel independently. The NIR radiance is subtracted from the radiance at each wavelength,
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but a wavelength-independent offset is also added.
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it is not clear how A and B were chosen, but an optimization for a case where in situ data is
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available would enable values to be found
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:param use_gdal (bool): 是否使用GDAL加速处理（需要GDAL可用且输入为文件路径或大数组）
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:param chunk_size (int): 已废弃，不再使用分块处理，改为逐波段处理
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:param water_mask (np.ndarray or str or None): 水域掩膜，1表示水域，0表示非水域
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可以是numpy数组、栅格文件路径(.dat/.tif)或shapefile路径(.shp)
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如果为None，则处理全图
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:param output_path (str or None): 输出文件路径，如果提供则保存校正后的图像
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如果为None，则不保存
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"""
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self.im_aligned = im_aligned
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self.NIR_lower = NIR_lower
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self.NIR_upper = NIR_upper
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self.A = A
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self.B = B
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self.use_gdal = use_gdal and GDAL_AVAILABLE
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self.chunk_size = chunk_size
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self.is_file_path = isinstance(im_aligned, str)
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self.output_path = output_path
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# 获取图像信息（需要在加载掩膜之前获取尺寸）
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if self.is_file_path:
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if not self.use_gdal:
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raise ValueError("输入为文件路径时，必须安装GDAL")
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self.dataset = gdal.Open(im_aligned, gdal.GA_ReadOnly)
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if self.dataset is None:
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raise ValueError(f"无法打开影像文件: {im_aligned}")
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self.height = self.dataset.RasterYSize
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self.width = self.dataset.RasterXSize
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self.n_bands = self.dataset.RasterCount
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else:
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self.dataset = None
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self.height = im_aligned.shape[0]
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self.width = im_aligned.shape[1]
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self.n_bands = im_aligned.shape[-1]
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# 加载水域掩膜（在获取图像尺寸之后）
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self.water_mask = self._load_water_mask(water_mask)
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def _load_water_mask(self, water_mask):
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"""
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加载水域掩膜
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:param water_mask: 可以是None、numpy数组、文件路径(.dat/.tif)或shapefile路径(.shp)
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:return: numpy数组或None，1表示水域，0表示非水域
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"""
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if water_mask is None:
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return None
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# 如果已经是numpy数组
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if isinstance(water_mask, np.ndarray):
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if water_mask.shape[:2] != (self.height, self.width):
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raise ValueError(f"掩膜尺寸 {water_mask.shape[:2]} 与图像尺寸 {(self.height, self.width)} 不匹配")
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return (water_mask > 0).astype(np.uint8) # 确保是0/1掩膜
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# 如果是文件路径
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if isinstance(water_mask, str):
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if not GDAL_AVAILABLE:
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raise ValueError("使用文件路径作为掩膜时，必须安装GDAL")
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# 检查是否为shapefile
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if water_mask.lower().endswith('.shp'):
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# 从shp文件创建掩膜
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if self.is_file_path:
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ref_path = self.im_aligned
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else:
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raise ValueError("输入为numpy数组时，无法从shp文件创建掩膜（需要参考栅格）")
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try:
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from osgeo import ogr
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ref_dataset = gdal.Open(ref_path, gdal.GA_ReadOnly)
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if ref_dataset is None:
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raise ValueError(f"无法打开参考栅格文件: {ref_path}")
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geotransform = ref_dataset.GetGeoTransform()
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projection = ref_dataset.GetProjection()
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width = ref_dataset.RasterXSize
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height = ref_dataset.RasterYSize
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# 创建内存中的栅格数据集
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mem_driver = gdal.GetDriverByName('MEM')
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mask_dataset = mem_driver.Create('', width, height, 1, gdal.GDT_Byte)
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mask_dataset.SetGeoTransform(geotransform)
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mask_dataset.SetProjection(projection)
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mask_band = mask_dataset.GetRasterBand(1)
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mask_band.Fill(0)
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# 打开shp文件
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shp_dataset = ogr.Open(water_mask)
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if shp_dataset is None:
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raise ValueError(f"无法打开shp文件: {water_mask}")
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layer = shp_dataset.GetLayer()
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gdal.RasterizeLayer(mask_dataset, [1], layer, burn_values=[1])
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water_mask_array = mask_band.ReadAsArray()
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ref_dataset = None
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mask_dataset = None
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shp_dataset = None
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return (water_mask_array > 0).astype(np.uint8)
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except Exception as e:
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raise ValueError(f"从shp文件创建掩膜时出错: {e}")
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else:
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# 栅格文件
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mask_dataset = gdal.Open(water_mask, gdal.GA_ReadOnly)
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if mask_dataset is None:
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raise ValueError(f"无法打开掩膜文件: {water_mask}")
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mask_array = mask_dataset.GetRasterBand(1).ReadAsArray()
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mask_dataset = None
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if mask_array.shape != (self.height, self.width):
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raise ValueError(f"掩膜尺寸 {mask_array.shape} 与图像尺寸 {(self.height, self.width)} 不匹配")
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return (mask_array > 0).astype(np.uint8)
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raise ValueError(f"不支持的掩膜类型: {type(water_mask)}")
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def _get_corrected_bands_numpy(self):
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"""
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使用numpy处理（用于小图像或GDAL不可用时）
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注意：由于输入已经是numpy数组，数据已在内存中。
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此方法通过逐波段处理，避免同时创建多个校正后的波段数组。
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内存峰值 = 原始数组 + NIR波段(2个) + 当前处理的波段(1个)
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"""
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# 预提取重复使用的NIR波段，避免在循环中重复访问
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# 这些波段会一直保存在内存中，因为它们需要用于所有波段的校正
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R_640 = self.im_aligned[:,:,self.NIR_lower]
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R_750 = self.im_aligned[:,:,self.NIR_upper]
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# 预计算常量部分
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diff_640_750 = R_640 - R_750
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corrected_bands = []
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# 获取水域掩膜（如果存在）
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water_mask_bool = self.water_mask.astype(bool) if self.water_mask is not None else None
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# 逐波段处理：每次只处理一个波段，处理完后立即添加到结果列表
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for i in tqdm(range(self.n_bands), desc="处理波段 (numpy)", total=self.n_bands):
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# 获取当前波段（这是数组视图，不是复制）
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R = self.im_aligned[:,:,i]
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# 优化计算：减少中间数组创建
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corrected_band = R - R_750 + self.A + self.B * diff_640_750
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# 使用np.maximum原地操作，将负值设为0
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np.maximum(corrected_band, 0, out=corrected_band)
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# 如果存在水域掩膜，只对水域区域应用校正
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if water_mask_bool is not None:
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corrected_band = np.where(water_mask_bool, corrected_band, R)
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# 立即添加到结果列表（corrected_band会保留在列表中）
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corrected_bands.append(corrected_band)
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return corrected_bands
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def _get_corrected_bands_gdal(self):
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"""
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使用GDAL逐波段处理，直接处理整个波段（不分块）
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内存峰值 = NIR波段(2个) + 当前处理的波段(1个) + 已处理的波段(累积在列表中)
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"""
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corrected_bands = []
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# 获取NIR波段对象（用于所有波段的校正）
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band_640 = self.dataset.GetRasterBand(self.NIR_lower + 1) # GDAL波段从1开始
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band_750 = self.dataset.GetRasterBand(self.NIR_upper + 1)
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# 先读取NIR波段（用于所有波段的校正，会一直保存在内存中）
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R_640 = band_640.ReadAsArray().astype(np.float32)
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R_750 = band_750.ReadAsArray().astype(np.float32)
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diff_640_750 = R_640 - R_750
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# 获取水域掩膜
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water_mask_bool = self.water_mask.astype(bool) if self.water_mask is not None else None
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# 逐波段处理：每次只读取和处理一个波段
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for i in tqdm(range(self.n_bands), desc="处理波段 (GDAL)", total=self.n_bands):
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# 读取当前波段（只加载一个波段到内存）
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current_band = self.dataset.GetRasterBand(i + 1)
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R = current_band.ReadAsArray().astype(np.float32)
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# 校正计算
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corrected_band = R - R_750 + self.A + self.B * diff_640_750
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np.maximum(corrected_band, 0, out=corrected_band)
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# 如果存在水域掩膜，只对水域区域应用校正
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if water_mask_bool is not None:
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corrected_band = np.where(water_mask_bool, corrected_band, R)
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# 添加到结果列表（corrected_band会保留在列表中）
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corrected_bands.append(corrected_band)
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# 释放当前波段数据（显式删除有助于及时释放内存）
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del R
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return corrected_bands
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def _get_corrected_bands_gdal_mem(self):
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"""使用GDAL内存驱动处理numpy数组，逐波段处理"""
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# 创建内存数据集
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driver = gdal.GetDriverByName('MEM')
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mem_dataset = driver.Create('', self.width, self.height, self.n_bands, gdal.GDT_Float32)
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# 将numpy数组写入内存数据集（显示进度）
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for i in tqdm(range(self.n_bands), desc="加载波段到内存", total=self.n_bands):
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band = mem_dataset.GetRasterBand(i + 1)
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band.WriteArray(self.im_aligned[:,:,i])
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band.FlushCache()
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# 临时保存原始dataset引用
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original_dataset = self.dataset
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self.dataset = mem_dataset
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try:
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# 使用逐波段处理方法
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result = self._get_corrected_bands_gdal()
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finally:
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# 恢复原始dataset
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self.dataset = original_dataset
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mem_dataset = None
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return result
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def _save_corrected_bands(self, corrected_bands):
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"""
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保存校正后的波段到文件（BSQ格式，ENVI格式）
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注意：为了节省内存，直接逐波段写入，不先堆叠成完整数组
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:param corrected_bands: 校正后的波段列表
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"""
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if not GDAL_AVAILABLE:
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raise ImportError("GDAL未安装，无法保存影像文件")
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if self.output_path is None:
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return
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import os
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# 确保输出目录存在
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output_dir = os.path.dirname(self.output_path)
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if output_dir and not os.path.exists(output_dir):
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os.makedirs(output_dir, exist_ok=True)
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# 从第一个波段获取尺寸信息（避免堆叠所有波段）
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if not corrected_bands:
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raise ValueError("校正后的波段列表为空")
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first_band = corrected_bands[0]
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height, width = first_band.shape
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n_bands = len(corrected_bands)
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# 获取地理变换和投影信息
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if self.is_file_path and self.dataset is not None:
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geotransform = self.dataset.GetGeoTransform()
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projection = self.dataset.GetProjection()
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else:
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# 如果没有地理信息，使用默认值
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geotransform = (0, 1, 0, 0, 0, -1)
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projection = ""
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# 强制使用ENVI格式（BSQ格式），确保文件扩展名为.bsq
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base_path, ext = os.path.splitext(self.output_path)
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# 如果扩展名不是.bsq，使用基础路径添加.bsq
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if ext.lower() != '.bsq':
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bsq_path = base_path + '.bsq'
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else:
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bsq_path = self.output_path
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# 使用ENVI驱动（默认就是BSQ格式）
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driver = gdal.GetDriverByName('ENVI')
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if driver is None:
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raise ValueError("无法创建ENVI格式文件，ENVI驱动不可用")
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# 创建ENVI格式数据集（会自动生成.hdr文件）
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dataset = driver.Create(bsq_path, width, height, n_bands, gdal.GDT_Float32)
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if dataset is None:
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raise ValueError(f"无法创建输出文件: {bsq_path}")
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try:
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# 设置地理变换和投影
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if geotransform:
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dataset.SetGeoTransform(geotransform)
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if projection:
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dataset.SetProjection(projection)
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# 直接逐波段写入（不先堆叠，节省内存）
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for i in tqdm(range(n_bands), desc="保存波段", total=n_bands):
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band = dataset.GetRasterBand(i + 1)
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# 直接从列表中获取波段并写入，避免创建完整数组
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band.WriteArray(corrected_bands[i])
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band.FlushCache()
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finally:
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dataset = None
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# 检查.hdr文件是否已创建
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hdr_path = bsq_path + '.hdr'
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if os.path.exists(hdr_path):
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print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)")
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print(f"头文件已保存至: {hdr_path}")
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else:
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print(f"校正后的图像已保存至: {bsq_path} (BSQ格式)")
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print(f"警告: 未检测到.hdr文件，但GDAL应该已自动创建")
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def get_corrected_bands(self):
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"""
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获取校正后的波段
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根据输入类型和大小自动选择最优处理方法
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:return: 校正后的波段列表
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"""
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# 如果输入是文件路径，使用GDAL直接读取
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if self.is_file_path:
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if self.use_gdal:
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corrected_bands = self._get_corrected_bands_gdal()
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else:
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raise ValueError("输入为文件路径时，必须安装GDAL")
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else:
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# 如果输入是numpy数组
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if self.use_gdal and self.height * self.width * self.n_bands > 100000000:
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# 大图像使用GDAL内存驱动逐波段处理
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corrected_bands = self._get_corrected_bands_gdal_mem()
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else:
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# 小图像使用numpy直接处理
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corrected_bands = self._get_corrected_bands_numpy()
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# 如果提供了输出路径，保存结果
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if self.output_path is not None:
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self._save_corrected_bands(corrected_bands)
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return corrected_bands
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def __del__(self):
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"""清理资源"""
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if self.dataset is not None and self.is_file_path:
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self.dataset = None
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