feat(sr2rgb): 完善影像转换处理逻辑.
This commit is contained in:
parent
fc5a29696a
commit
ff1dea2324
@ -104,8 +104,8 @@ dependencies:
|
||||
- geckodriver=0.36.0=h127b8e1_0
|
||||
- geopandas=1.1.1=pyhd8ed1ab_1
|
||||
- geopandas-base=1.1.1=pyha770c72_1
|
||||
- geos=3.13.1=h9ea8674_0
|
||||
- geotiff=1.7.4=h86c3423_2
|
||||
- geos=3.14.0=hdade9fe_0
|
||||
- geotiff=1.7.4=h73469f5_4
|
||||
- geoviews=1.14.1=hd8ed1ab_0
|
||||
- geoviews-core=1.14.1=pyha770c72_0
|
||||
- giflib=5.2.2=h64bf75a_0
|
||||
@ -117,7 +117,7 @@ dependencies:
|
||||
- h2=4.3.0=pyhcf101f3_0
|
||||
- h5netcdf=1.6.4=pyhd8ed1ab_0
|
||||
- h5py=3.14.0=nompi_py312h03cd2ba_101
|
||||
- harmony-py=1.2.0=pyhd8ed1ab_0
|
||||
- harmony-py=0.4.14=pyhd8ed1ab_0
|
||||
- hdf4=4.2.15=h5557f11_7
|
||||
- hdf5=1.14.6=nompi_he30205f_103
|
||||
- holoviews=1.21.0=pyhd8ed1ab_0
|
||||
@ -126,6 +126,7 @@ dependencies:
|
||||
- httpx=0.28.1=pyhd8ed1ab_0
|
||||
- hvplot=0.12.1=pyhd8ed1ab_0
|
||||
- hyperframe=6.1.0=pyhd8ed1ab_0
|
||||
- icu=75.1=he0c23c2_0
|
||||
- idna=3.10=pyhd8ed1ab_1
|
||||
- imagecodecs=2025.8.2=py312h424859f_4
|
||||
- imageio=2.37.0=pyhfb79c49_0
|
||||
@ -168,7 +169,7 @@ dependencies:
|
||||
- lerc=4.0.0=h6470a55_1
|
||||
- libabseil=20250127.1=cxx17_h4eb7d71_0
|
||||
- libaec=1.1.4=h20038f6_0
|
||||
- libarchive=3.8.1=gpl_h1ca5a36_100
|
||||
- libarchive=3.8.1=gpl_h26aea39_101
|
||||
- libarrow=20.0.0=h7ea4809_8_cuda
|
||||
- libarrow-acero=20.0.0=h7d8d6a5_8_cuda
|
||||
- libarrow-dataset=20.0.0=h7d8d6a5_8_cuda
|
||||
@ -188,13 +189,13 @@ dependencies:
|
||||
- libfreetype=2.14.1=h57928b3_0
|
||||
- libfreetype6=2.14.1=hdbac1cb_0
|
||||
- libgcc=15.2.0=h1383e82_7
|
||||
- libgdal-core=3.10.3=h228a343_13
|
||||
- libgdal-hdf4=3.10.3=ha47b6c4_13
|
||||
- libgdal-core=3.10.3=haf333d4_21
|
||||
- libgdal-hdf4=3.10.3=ha47b6c4_21
|
||||
- libgomp=15.2.0=h1383e82_7
|
||||
- libgoogle-cloud=2.36.0=hf249c01_1
|
||||
- libgoogle-cloud-storage=2.36.0=he5eb982_1
|
||||
- libgrpc=1.71.0=h8c3449c_1
|
||||
- libhwloc=2.12.1=default_h88281d1_1000
|
||||
- libhwloc=2.12.1=default_h64bd3f2_1002
|
||||
- libhwy=1.3.0=ha71e874_1
|
||||
- libiconv=1.18=hc1393d2_2
|
||||
- libjpeg-turbo=3.1.0=h2466b09_0
|
||||
@ -202,14 +203,14 @@ dependencies:
|
||||
- libkml=1.3.0=h538826c_1021
|
||||
- liblapack=3.9.0=35_hf9ab0e9_mkl
|
||||
- liblzma=5.8.1=h2466b09_2
|
||||
- libnetcdf=4.9.3=nompi_ha45073a_102
|
||||
- libnetcdf=4.9.3=nompi_h7d90bef_103
|
||||
- libparquet=20.0.0=ha850022_8_cuda
|
||||
- libpng=1.6.50=h7351971_1
|
||||
- libprotobuf=5.29.3=hd33f5f0_2
|
||||
- libre2-11=2025.06.26=habfad5f_0
|
||||
- librttopo=1.1.0=hbfc9ebc_18
|
||||
- librttopo=1.1.0=h5ff11c1_19
|
||||
- libsodium=1.0.20=hc70643c_0
|
||||
- libspatialite=5.1.0=h378fb81_14
|
||||
- libspatialite=5.1.0=gpl_h3bf7137_118
|
||||
- libsqlite=3.50.4=hf5d6505_0
|
||||
- libssh2=1.11.1=h9aa295b_0
|
||||
- libthrift=0.21.0=hbe90ef8_0
|
||||
@ -218,7 +219,9 @@ dependencies:
|
||||
- libwebp-base=1.6.0=h4d5522a_0
|
||||
- libwinpthread=12.0.0.r4.gg4f2fc60ca=h57928b3_10
|
||||
- libxcb=1.17.0=h0e4246c_0
|
||||
- libxml2=2.13.8=h741aa76_1
|
||||
- libxml2=2.15.0=ha29bfb0_1
|
||||
- libxml2-16=2.15.0=h06f855e_1
|
||||
- libxml2-devel=2.15.0=ha29bfb0_1
|
||||
- libzip=1.11.2=h3135430_0
|
||||
- libzlib=1.3.1=h2466b09_2
|
||||
- libzopfli=1.0.3=h0e60522_0
|
||||
@ -280,7 +283,7 @@ dependencies:
|
||||
- parso=0.8.5=pyhcf101f3_0
|
||||
- partd=1.4.2=pyhd8ed1ab_0
|
||||
- patsy=1.0.1=pyhd8ed1ab_1
|
||||
- pcre2=10.45=h99c9b8b_0
|
||||
- pcre2=10.46=h3402e2f_0
|
||||
- pickleshare=0.7.5=pyhd8ed1ab_1004
|
||||
- pillow=11.3.0=py312h5ee8bfe_3
|
||||
- pip=25.2=pyh8b19718_0
|
||||
@ -288,7 +291,7 @@ dependencies:
|
||||
- pockets=0.9.1=pyhd8ed1ab_1
|
||||
- pqdm=0.2.0=pyhd8ed1ab_1
|
||||
- progressbar2=4.2.0=pyhd8ed1ab_0
|
||||
- proj=9.6.2=h7990399_2
|
||||
- proj=9.7.0=h9080b7b_0
|
||||
- prometheus_client=0.23.1=pyhd8ed1ab_0
|
||||
- prompt-toolkit=3.0.52=pyha770c72_0
|
||||
- prompt_toolkit=3.0.52=hd8ed1ab_0
|
||||
@ -312,7 +315,7 @@ dependencies:
|
||||
- pyogrio=0.11.0=py312h6e88f47_0
|
||||
- pyopenssl=25.1.0=pyhd8ed1ab_0
|
||||
- pyparsing=3.2.5=pyhcf101f3_0
|
||||
- pyproj=3.7.2=py312h235ce7f_1
|
||||
- pyproj=3.7.2=py312habbd053_2
|
||||
- pyresample=1.34.2=py312h275cf98_0
|
||||
- pyshp=3.0.2=pyhd8ed1ab_0
|
||||
- pysocks=1.7.1=pyh09c184e_7
|
||||
@ -320,7 +323,7 @@ dependencies:
|
||||
- pystac-client=0.9.0=pyhd8ed1ab_0
|
||||
- python=3.12.11=h3f84c4b_0_cpython
|
||||
- python-cmr=0.13.0=pyhff2d567_1
|
||||
- python-dateutil=2.9.0.post0=pyhe01879c_2
|
||||
- python-dateutil=2.8.2=pyhd8ed1ab_0
|
||||
- python-dotenv=0.20.0=pyhd8ed1ab_0
|
||||
- python-fastjsonschema=2.21.2=pyhe01879c_0
|
||||
- python-gil=3.12.11=hd8ed1ab_0
|
||||
@ -337,7 +340,7 @@ dependencies:
|
||||
- pyyaml=6.0.3=py312h05f76fc_0
|
||||
- pyzmq=27.1.0=py312hbb5da91_0
|
||||
- qhull=2020.2=hc790b64_5
|
||||
- rasterio=1.4.3=py312h9aeec68_2
|
||||
- rasterio=1.4.3=py312h11f88aa_3
|
||||
- rav1e=0.7.1=ha073cba_3
|
||||
- ray-core=2.49.2=py312h6652516_2
|
||||
- ray-default=2.49.2=py312h8c80c70_2
|
||||
@ -360,7 +363,7 @@ dependencies:
|
||||
- selenium-manager=4.36.0=h18a1a76_0
|
||||
- send2trash=1.8.3=pyh5737063_1
|
||||
- setuptools=80.9.0=pyhff2d567_0
|
||||
- shapely=2.0.7=py312h3f81574_1
|
||||
- shapely=2.1.2=py312ha0f8e3e_0
|
||||
- six=1.17.0=pyhe01879c_1
|
||||
- smart_open=7.3.1=pyhcf101f3_0
|
||||
- snappy=1.2.2=h7fa0ca8_0
|
||||
@ -433,7 +436,4 @@ dependencies:
|
||||
- zlib-ng=2.2.5=h1608b31_0
|
||||
- zstandard=0.25.0=py312he5662c2_0
|
||||
- zstd=1.5.7=hbeecb71_2
|
||||
- pip:
|
||||
- beautifulsoup4==4.12.3
|
||||
|
||||
prefix: "D:\\program\\miniforge3\\envs\\lpdaac"
|
||||
prefix: D:\program\miniforge3\envs\lpdaac
|
||||
|
||||
@ -1,5 +1,9 @@
|
||||
"""
|
||||
使用 GDAL BuildVRT 与 Translate 快速将影像批量镶嵌并转换为 8bit RGB (Light version)
|
||||
|
||||
1. 将输入目录下的所有 tif 文件合并为一个 VRT 文件;
|
||||
2. 采用 GDAL ComputeStatistics 进行快速统计计算, 并根据指定的百分比截断值计算有效数据范围;
|
||||
3. 将 VRT 文件转换为 8bit RGB 格式的 COG 文件, 并保存到指定目录.
|
||||
"""
|
||||
|
||||
import os
|
||||
@ -21,10 +25,84 @@ gdal.SetConfigOption("GDAL_NUM_THREADS", "ALL_CPUS")
|
||||
gdal.SetConfigOption("GDAL_CACHEMAX", "1024")
|
||||
|
||||
|
||||
def vrt_to_8bit_simple(input_dir: str | Path, output_path: str | Path, num: int = 2):
|
||||
def compute_band_stats(band: gdal.Band, approx_ok: bool = True, no_data: float = np.nan, percentile: float = 2.0):
|
||||
"""
|
||||
使用 GDAL ComputeStatistics 计算波段的统计信息
|
||||
|
||||
实现类似 QGIS 图层符号化中的累计计数削减效果
|
||||
|
||||
Parameters
|
||||
----------
|
||||
band : gdal.Band
|
||||
GDAL 波段对象
|
||||
approx_ok : bool, optional
|
||||
是否允许近似统计, 速度快, by default True
|
||||
no_data : float, optional
|
||||
无效值, 默认 np.nan, by default np.nan
|
||||
percentile : float, optional
|
||||
百分比截断值 (2 表示 2%-98% 范围), by default 2.0
|
||||
|
||||
Returns
|
||||
-------
|
||||
tuple
|
||||
(min_val, max_val)
|
||||
"""
|
||||
try:
|
||||
# 使用 GDAL 的 ComputeStatistics 计算统计信息
|
||||
stats = band.ComputeStatistics(approx_ok)
|
||||
if stats is not None and len(stats) >= 2:
|
||||
min_val, max_val = stats[0], stats[1]
|
||||
|
||||
# 如果需要更精确的百分比统计, 使用直方图
|
||||
if percentile > 0:
|
||||
# 创建直方图
|
||||
hist_min = min_val
|
||||
hist_max = max_val
|
||||
bucket_count = 256 # 直方图桶数
|
||||
|
||||
# 计算直方图
|
||||
hist = band.GetHistogram(
|
||||
hist_min, hist_max, bucket_count,
|
||||
include_out_of_range=False, approx_ok=True
|
||||
)
|
||||
|
||||
if hist and len(hist) > 0:
|
||||
# 计算累积直方图
|
||||
hist_array = np.array(hist, dtype=np.float32)
|
||||
cum_hist = np.cumsum(hist_array)
|
||||
total = cum_hist[-1]
|
||||
|
||||
# 找到百分比位置
|
||||
# 考虑到无效值为 -9999, 所以直接从 0 开始, 尽量还原原始数据范围
|
||||
if no_data <= 0:
|
||||
lower_thresh = 0.0
|
||||
else:
|
||||
lower_thresh = total * (percentile / 100.0)
|
||||
upper_thresh = total * (1 - percentile / 100.0)
|
||||
|
||||
# 找到对应的值
|
||||
lower_idx = np.searchsorted(cum_hist, lower_thresh)
|
||||
upper_idx = np.searchsorted(cum_hist, upper_thresh)
|
||||
|
||||
if lower_idx < len(hist_array) and upper_idx < len(hist_array):
|
||||
# 将索引映射回实际值
|
||||
bin_width = (hist_max - hist_min) / bucket_count
|
||||
min_val = hist_min + lower_idx * bin_width
|
||||
max_val = hist_min + upper_idx * bin_width
|
||||
|
||||
return min_val, max_val
|
||||
except Exception as e:
|
||||
logging.warning(f"计算统计信息失败: {str(e)}")
|
||||
|
||||
# 如果失败, 返回默认值
|
||||
return 0.0, 1.0
|
||||
|
||||
|
||||
def vrt_to_8bit_simple(input_dir: str | Path, output_path: str | Path,
|
||||
no_data: float = np.nan, percentile: float = 1.0):
|
||||
"""
|
||||
使用 gdal.Translate 将指定目录下的 tif 文件合并并转换为 8bit
|
||||
NaN 值将被赋值为 0
|
||||
使用 GDAL 内置统计功能快速计算并转换, NaN 值将被赋值为 0
|
||||
|
||||
Parameters
|
||||
----------
|
||||
@ -32,111 +110,89 @@ def vrt_to_8bit_simple(input_dir: str | Path, output_path: str | Path, num: int
|
||||
输入 TIF 文件所在目录
|
||||
output_path : str | Path
|
||||
输出文件路径
|
||||
num : int, optional
|
||||
拉伸的百分比范围, 默认2%到98%
|
||||
no_data : float, optional
|
||||
输入影像中的无效值, 默认 np.nan
|
||||
percentile : float, optional
|
||||
百分比截断值, 默认 0% 到 99%
|
||||
"""
|
||||
input_dir = Path(input_dir)
|
||||
output_path = Path(output_path)
|
||||
|
||||
|
||||
# 1. 获取所有tif文件
|
||||
tif_files = [
|
||||
str(f)
|
||||
for f in input_dir.iterdir()
|
||||
if f.is_file() and f.suffix.lower() == ".tif"
|
||||
]
|
||||
|
||||
|
||||
if not tif_files:
|
||||
logging.warning(f"{input_dir} 中没有找到 tif 文件.")
|
||||
return
|
||||
|
||||
|
||||
logging.info(f"1) 找到 {len(tif_files)} 个 tif 文件")
|
||||
|
||||
vrt_path = input_dir / "temp.vrt"
|
||||
|
||||
vrt_path = input_dir / "merged.vrt"
|
||||
vrt_ds = None
|
||||
|
||||
try:
|
||||
# 2. 创建VRT
|
||||
logging.info("2) 构建 VRT 镶嵌...")
|
||||
vrt_ds = gdal.BuildVRT(str(vrt_path), tif_files)
|
||||
|
||||
vrt_options = gdal.BuildVRTOptions(
|
||||
srcNodata=np.nan, # 输入影像中的无效值, 设为 NaN 防止拼接处存在缝隙
|
||||
VRTNodata=no_data, # 输出 VRT 中的无效值
|
||||
)
|
||||
vrt_ds = gdal.BuildVRT(str(vrt_path), tif_files, options=vrt_options)
|
||||
|
||||
if vrt_ds is None:
|
||||
logging.error("构建 VRT 失败.")
|
||||
return
|
||||
|
||||
|
||||
# 获取波段数与影像尺寸
|
||||
num_bands = vrt_ds.RasterCount
|
||||
width = vrt_ds.RasterXSize
|
||||
height = vrt_ds.RasterYSize
|
||||
logging.info(f"波段数: {num_bands}, 影像尺寸: {width}x{height}")
|
||||
|
||||
# 3. 计算每个波段的统计信息并处理 NaN 值
|
||||
logging.info("3) 计算影像统计信息...")
|
||||
|
||||
# 3. 使用 GDAL 快速计算统计信息
|
||||
logging.info("3) 使用 GDAL 计算影像统计信息...")
|
||||
scale_params = []
|
||||
for band_idx in range(1, num_bands + 1):
|
||||
band = vrt_ds.GetRasterBand(band_idx)
|
||||
|
||||
# 读取数据块以计算统计 (避免加载全部数据)
|
||||
block_size = 1024
|
||||
stats_min = []
|
||||
stats_max = []
|
||||
|
||||
for y in range(0, vrt_ds.RasterYSize, block_size):
|
||||
height = min(block_size, vrt_ds.RasterYSize - y)
|
||||
for x in range(0, vrt_ds.RasterXSize, block_size):
|
||||
width = min(block_size, vrt_ds.RasterXSize - x)
|
||||
|
||||
data = band.ReadAsArray(x, y, width, height)
|
||||
if data is not None:
|
||||
# 计算非NaN的最小最大值
|
||||
valid_data = data[~np.isnan(data)]
|
||||
# 读取2%-98%的有效数据范围
|
||||
if len(valid_data) > 0:
|
||||
stats_min.append(np.nanpercentile(valid_data, num))
|
||||
stats_max.append(np.nanpercentile(valid_data, 100 - num))
|
||||
|
||||
if stats_min and stats_max:
|
||||
min_val = min(stats_min)
|
||||
max_val = max(stats_max)
|
||||
logging.info(f"波段 {band_idx}: 有效值范围 [{min_val:.4f}, {max_val:.4f}]")
|
||||
scale_params.append([min_val, max_val, 0, 255])
|
||||
else:
|
||||
logging.warning(f"波段 {band_idx}: 未找到有效数据, 使用默认范围 [0, 1]")
|
||||
scale_params.append([0, 1, 0, 255])
|
||||
|
||||
# 4. 使用gdal_translate转换为8bit, NaN值设为0
|
||||
logging.info("4) 使用 gdal_translate 转换为 8bit (NaN->0)...")
|
||||
# 使用 GDAL 内置函数计算统计
|
||||
min_val, max_val = compute_band_stats(
|
||||
band, no_data=no_data, percentile=percentile)
|
||||
logging.info(
|
||||
f"波段 {band_idx}: 有效值范围 [{min_val:.4f}, {max_val:.4f}]")
|
||||
# 将有效值映射到 1-255, 保留 0 作为 NoData 专用值
|
||||
# 避免暗部像素 (如水体、阴影) 会被映射为 0, 从而被误判为透明缺失
|
||||
scale_params.append([min_val, max_val, 1, 255])
|
||||
|
||||
# 4. 使用gdal_translate转换为8bit, 无效值统一设为0
|
||||
if output_path.exists():
|
||||
logging.warning(f"结果文件已存在: {output_path}")
|
||||
return
|
||||
logging.info("4) 使用 gdal_translate 转换为 8bit COG (NaN->0)...")
|
||||
translate_options = gdal.TranslateOptions(
|
||||
format="COG", # 输出为 COG 格式, 自动构建金字塔
|
||||
scaleParams=scale_params,
|
||||
outputType=gdal.GDT_Byte,
|
||||
noData=0, # 将NaN转换为0
|
||||
noData=0, # 输出 NoData 设为 0
|
||||
creationOptions=[
|
||||
"COMPRESS=DEFLATE",
|
||||
"TILED=YES",
|
||||
"COMPRESS=DEFLATE",
|
||||
"ZLEVEL=4", # DEFLATE 压缩级别, 支持 1-9, 默认为 6
|
||||
"PREDICTOR=2", # 差值预测, 利于影像压缩
|
||||
"NUM_THREADS=ALL_CPUS",
|
||||
"BIGTIFF=IF_SAFER",
|
||||
"PHOTOMETRIC=RGB",
|
||||
]
|
||||
# "TILED=YES", # COG 格式自带分块, 不支持手动设置
|
||||
# "PHOTOMETRIC=RGB", # COG 格式不支持 photometric 参数
|
||||
],
|
||||
callback=gdal.TermProgress_nocb # 进度回调, 不显示进度条
|
||||
)
|
||||
|
||||
gdal.Translate(str(output_path), vrt_ds, options=translate_options)
|
||||
logging.info(f"已保存: {output_path}")
|
||||
|
||||
# 5. 构建金字塔 (基于输出文件)
|
||||
logging.info("5) 构建金字塔...")
|
||||
# 释放VRT数据集，确保文件句柄释放
|
||||
|
||||
# 释放VRT数据集, 确保文件句柄释放
|
||||
vrt_ds = None
|
||||
|
||||
# 打开生成的输出文件进行更新
|
||||
out_ds = gdal.Open(str(output_path), gdal.GA_Update)
|
||||
if out_ds:
|
||||
try:
|
||||
# 构建金字塔: 2, 4, 8, 16...
|
||||
# 这里根据影像大小自适应或者固定层级
|
||||
out_ds.BuildOverviews("AVERAGE", [2, 4, 8, 16])
|
||||
logging.info("金字塔构建完成")
|
||||
finally:
|
||||
out_ds = None
|
||||
else:
|
||||
logging.error(f"无法打开文件构建金字塔: {output_path}")
|
||||
|
||||
except Exception as e:
|
||||
logging.error(f"处理过程中出现异常: {str(e)}")
|
||||
@ -150,28 +206,24 @@ def vrt_to_8bit_simple(input_dir: str | Path, output_path: str | Path, num: int
|
||||
def main(input_dir, output_path):
|
||||
input_dir = Path(input_dir)
|
||||
output_path = Path(output_path)
|
||||
|
||||
|
||||
output_root = output_path.parent
|
||||
os.makedirs(output_root, exist_ok=True)
|
||||
|
||||
|
||||
log_file = output_root / "sr2rgb_light.log"
|
||||
setup_logging(str(log_file))
|
||||
|
||||
|
||||
logging.info("开始批量处理 (Light Mode)...")
|
||||
logging.info(f"输入目录: {input_dir}")
|
||||
logging.info(f"输出文件: {output_path}")
|
||||
|
||||
if output_path.exists():
|
||||
logging.warning(f"结果文件已存在: {output_path}")
|
||||
return
|
||||
|
||||
vrt_to_8bit_simple(input_dir, output_path)
|
||||
vrt_to_8bit_simple(input_dir, output_path, no_data=-9999.0, percentile=1.0)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 输入目录: 包含分块tif影像的根目录
|
||||
input_root = Path(r"D:\CVEOdata\RS_Data\2025_S2")
|
||||
input_root = Path(r"D:\CVEOdata\RS_Data\2025_S2_COG")
|
||||
# 输出目录: 存放最终RGB镶嵌结果的目录
|
||||
output_root = Path(r"D:\CVEOdata\RS_Data\2025_S2_RGB")
|
||||
rgb_file = output_root / "Hubei_Sentinel-2_2025_RGB.tif"
|
||||
main(input_root, rgb_file)
|
||||
output_root = Path(r"D:\CVEOdata\RS_Data\2025_S2_RGB_COG")
|
||||
rgb_file = output_root / "Hubei_Sentinel-2_2025_RGB_COG.tif"
|
||||
main(input_root, rgb_file)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user