def rasterRW(fn, raster_lyr, raster_pred, raster_lyr_w):
gdal.UseExceptions()
'''打开栅格数据'''
try:
src_ds = gdal.Open(os.path.join(fn, raster_lyr))
except RuntimeError as e:
print('Unable to open %s' % os.path.join(fn, raster_lyr))
print(e)
sys.exit(1)
print("metadata:", src_ds.GetMetadata())
'''初始化输出栅格'''
driver = gdal.GetDriverByName('GTiff')
print(src_ds.RasterXSize, src_ds.RasterYSize)
out_raster = driver.Create(os.path.join(fn, raster_lyr_w), col, row, 1,
gdal.GDT_Float64)
# out_raster=driver.Create(os.path.join(fn,raster_lyr_w),row,col,1,gdal.GDT_CInt16)
out_raster.SetProjection(src_ds.GetProjection()) #设置投影与参考栅格同
out_raster.SetGeoTransform(src_ds.GetGeoTransform()) #配置地理转换与参考栅格同
'''将数组传给栅格波段，为栅格值'''
out_band = out_raster.GetRasterBand(1)
out_band.WriteArray(raster_pred)
'''设置overview'''
overviews = pb.compute_overview_levels(out_raster.GetRasterBand(1))
out_raster.BuildOverviews('average', overviews)
'''清理缓存与移除数据源'''
out_band.FlushCache()
out_band.ComputeStatistics(False)
del src_ds, out_raster, out_band
def rasterCal(fn, raster_lyr, raster_lyr_w):
gdal.UseExceptions()
'''打开栅格数据'''
try:
src_ds = gdal.Open(os.path.join(fn, raster_lyr))
except RuntimeError as e:
print('Unable to open %s' % os.path.join(fn, raster_lyr))
print(e)
sys.exit(1)
print("metadata:", src_ds.GetMetadata())
'''获取所有波段'''
srcband = []
for band_num in range(1, 8):
try:
srcband.append(src_ds.GetRasterBand(band_num))
except RuntimeError as e:
print('Band ( %i ) not found' % band_num)
print(e)
sys.exit(1)
print(srcband)
'''获取用于NDVI计算的红和近红波段数组,并计算ndvi'''
red = srcband[3].ReadAsArray().astype(np.float)
nir = srcband[4].ReadAsArray()
red = np.ma.masked_where(nir + red == 0, red) #确定分母不为零
ndvi = (nir - red) / (nir + red)
ndvi = ndvi.filled(-99)
print(ndvi.shape, ndvi.std(), ndvi.max(), ndvi.min(), ndvi.mean())
'''初始化输出栅格'''
driver = gdal.GetDriverByName('GTiff')
if os.path.exists(os.path.join(fn, raster_lyr_w)):
driver.Delete(os.path.join(fn, raster_lyr_w))
out_raster = driver.Create(os.path.join(fn,
raster_lyr_w), src_ds.RasterXSize,
src_ds.RasterYSize, 1, gdal.GDT_Float64)
out_raster.SetProjection(src_ds.GetProjection()) #设置投影与参考栅格同
out_raster.SetGeoTransform(src_ds.GetGeoTransform()) #配置地理转换与参考栅格同
'''将数组传给栅格波段，为栅格值'''
out_band = out_raster.GetRasterBand(1)
out_band.WriteArray(ndvi)
'''设置overview'''
overviews = pb.compute_overview_levels(out_raster.GetRasterBand(1))
out_raster.BuildOverviews('average', overviews)
'''清理缓存与移除数据源'''
out_band.FlushCache()
out_band.ComputeStatistics(False)
del src_ds, out_raster, out_band
return ndvi
# Script to compute NDVI.
import os
import numpy as np
from osgeo import gdal
import ospybook as pb
os.chdir(r'D:\osgeopy-data\Massachusetts')
in_fn = 'm_4207162_ne_19_1_20140718_20140923.tif'
out_fn = 'ndvi2.tif'
ds = gdal.Open(in_fn)
red = ds.GetRasterBand(1).ReadAsArray().astype(np.float)
nir = ds.GetRasterBand(4).ReadAsArray()
# Mask the red band.
red = np.ma.masked_where(nir + red == 0, red)
# Do the calculation.
ndvi = (nir - red) / (nir + red)
# Fill the empty cells.
ndvi = ndvi.filled(-99)
# Set NoData to the fill value when creating the new raster.
out_ds = pb.make_raster(ds, out_fn, ndvi, gdal.GDT_Float32, -99)
overviews = pb.compute_overview_levels(out_ds.GetRasterBand(1))
out_ds.BuildOverviews('average', overviews)
del ds, out_ds
folder = r"D:\osgeopy-data\Landsat\Utah"
raster_fns = ["LE70380322000181EDC02_60m.tif", "LE70380322000181EDC02_TIR_60m.tif"]
out_fn = "kmeans_prediction_60m.tif"
# Function from list 10.10.
def stack_bands(filenames):
"""Returns a 3D array containing all band data from all files."""
bands = []
for fn in raster_fns:
ds = gdal.Open(fn)
for i in range(1, ds.RasterCount + 1):
bands.append(ds.GetRasterBand(i).ReadAsArray())
return np.dstack(bands)
# Stack the bands and run the analysis.
os.chdir(folder)
data = pb.stack_bands(raster_fns)
classes, centers = spectral.kmeans(data)
# Save the output.
ds = gdal.Open(raster_fns[0])
out_ds = pb.make_raster(ds, out_fn, classes, gdal.GDT_Byte)
levels = pb.compute_overview_levels(out_ds.GetRasterBand(1))
out_ds.BuildOverviews("NEAREST", levels)
out_ds.FlushCache()
out_ds.GetRasterBand(1).ComputeStatistics(False)
del out_ds, ds
data = pb.stack_bands(raster_fns)
# Sample the satellite data at the coordinates from the csv.
sample = data[rows, cols, :]
# Fit the classification tree.
clf = tree.DecisionTreeClassifier(max_depth=5)
clf = clf.fit(sample, classes)
# Apply the new classification tree model to the satellite data.
rows, cols, bands = data.shape
data2d = np.reshape(data, (rows * cols, bands))
prediction = clf.predict(data2d)
prediction = np.reshape(prediction, (rows, cols))
# Set the pixels with no valid satellite data to 0.
prediction[np.sum(data, 2) == 0] = 0
# Save the output.
predict_ds = pb.make_raster(ds, out_fn, prediction, gdal.GDT_Byte, 0)
predict_ds.FlushCache()
levels = pb.compute_overview_levels(predict_ds.GetRasterBand(1))
predict_ds.BuildOverviews('NEAREST', levels)
# Apply the color table from the SWReGAP landcover raster.
gap_ds = gdal.Open(gap_fn)
colors = gap_ds.GetRasterBand(1).GetRasterColorTable()
predict_ds.GetRasterBand(1).SetRasterColorTable(colors)
del ds
def rasterR(fn, raster_lyr, raster_lyr_w, bandNum):
gdal.UseExceptions()
'''打开栅格数据'''
try:
src_ds = gdal.Open(os.path.join(fn, raster_lyr))
except RuntimeError as e:
print('Unable to open %s' % os.path.join(fn, raster_lyr))
print(e)
sys.exit(1)
print("metadata:", src_ds.GetMetadata())
'''获取所有波段'''
srcband = []
for band_num in range(1, bandNum + 1):
try:
srcband.append(src_ds.GetRasterBand(band_num))
except RuntimeError as e:
print('Band ( %i ) not found' % band_num)
print(e)
sys.exit(1)
print(srcband)
'''获取RGB并图表打印查看'''
rsData = {}
for key in range(bandNum):
rsData[key] = srcband[key].ReadAsArray().astype(np.int32)
for key in range(bandNum):
print(rsData[key].shape)
print(DataFrame(rsData[key].flatten()).describe()
) #count数量/mean均值/std标准差/min最小值/25%下四分位/50%中位数/75%上四分位/max最大值
rgb = np.stack((rsData[0], rsData[1], rsData[2]), axis=2)
# rgb[rgb==256]=100 #如果有些值不正确，或者需调整某些值时，可以进行替换
# print(rgb)
fig = plt.figure(figsize=(20, 12))
ax = fig.add_subplot(111)
ax.imshow(rgb)
plt.show()
print(rgb)
print(rgb.shape)
'''将RGB转换为HSV,并图表打印查看'''
vfunc = np.vectorize(
colorsys.rgb_to_hsv) # HSV代表色调(Hue)，饱和度(Saturation)和值(Value)
# vfunc=np.vectorize(colorsys.rgb_to_hls) # HSL代表色调(Hue)，饱和度(Saturation)和亮度(Lightness)
H, S, V = vfunc(rsData[0], rsData[1], rsData[2])
HSV = np.stack((H, S, V), axis=2)
print(HSV)
print(HSV.shape)
#HSV = cv2.cvtColor(rgb, cv2.COLOR_BGR2HSV)
# print(HSV.T.shape)
print(DataFrame(H.flatten()).describe())
fig = plt.figure(figsize=(20, 12))
ax = fig.add_subplot(111)
ax.imshow(HSV)
plt.show()
'''将单独的色彩向量写入栅格，此次写入的是H色调值'''
'''初始化输出栅格'''
driver = gdal.GetDriverByName('GTiff')
out_raster = driver.Create(os.path.join(fn,
raster_lyr_w), src_ds.RasterXSize,
src_ds.RasterYSize, 1, gdal.GDT_Float64)
out_raster.SetProjection(src_ds.GetProjection()) #设置投影与参考栅格同
out_raster.SetGeoTransform(src_ds.GetGeoTransform()) #配置地理转换与参考栅格同
'''将数组传给栅格波段，为栅格值'''
out_band = out_raster.GetRasterBand(1)
H[H > 0.9] = 0.01 #根据情况调整色调值，或进行标识
H[H == 0] = 1 #特殊值处理，例如空白的值
out_band.WriteArray(1 - H)
# out_band.WriteArray(V) #也可自行尝试写入其它值
'''设置overview，本部分未使用'''
overviews = pb.compute_overview_levels(out_raster.GetRasterBand(1))
overviews = pb.compute_overview_levels(srcband[0])
src_ds.BuildOverviews('average', overviews)
'''清理缓存与移除数据源'''
out_band.FlushCache()
out_band.ComputeStatistics(False)
del src_ds, out_raster, out_band
return rsData
