def World2Pixel(geoMatrix, x, y):
#使用gdal库的geomatrix对象【gdal.GetGeoTransform()】计算地理坐标的像素位置
ulX = geoMatrix[0]
ulY = geoMatrix[3]
xDist = geoMatrix[1]
yDist = geoMatrix[5]
pixel = int((x - ulX) / xDist)
line = int((ulY - y) / abs(yDist))
return (pixel, line)
raster = r"D:\Program Files\Python学习文档\samples\FalseColor\stretched.tif"
shp = r"D:\Program Files\Python学习文档\samples\hancock\hancock.shp"
output = r"D:\Program Files\Python学习文档\samples\FalseColor\clip3.tif"
srcArray = gdal_array.LoadFile(raster)
#同时载入gdal库的图片从而获取geotransform（世界文件）
srcImage = gdal.Open(raster)
geoTrans = srcImage.GetGeoTransform()
reader = shapefile.Reader(shp)
#将图层扩张转化为图片像素坐标
minX, minY, maxX, maxY = reader.bbox
ulX, ulY = World2Pixel(geoTrans, minX, maxY)
lrX, lrY = World2Pixel(geoTrans, maxX, minY)
#计算新图片的像素尺寸
pxWidth = int(lrX - ulX)
pxHeight = int(lrY - ulY)
clip = srcArray[:, ulY:lrY, ulX:lrX]
#为图片创建一个新的geomatrix对象以便附加地理参考数据
geoTrans = list(geoTrans)
geoTrans[0] = minX
type=str,
required=True,
help='the title of the image')
parser.add_argument('--output',
'-o',
type=str,
required=True,
help='the output image file')
args = parser.parse_args()
true_file = args.true.expanduser()
pred_file = args.predict.expanduser()
band_ix = args.band
title = args.title
output_name = args.output
ix = gdal_array.LoadFile(str(true_file))
iy = gdal_array.LoadFile(str(pred_file))
if ix.ndim == 3:
ix = ix[band_ix]
iy = iy[band_ix]
# 单波段数据
assert ix.ndim == 2 and iy.ndim == 2
x = ix[:500, :500].flatten()
y = iy[:500, :500].flatten()
r2 = r2_score(x, y)
xy = np.vstack([x, y])
z = gaussian_kde(xy)(xy)
idx = z.argsort()
"""Perform a simple difference image change detection on a
'before' and 'after' image."""
# Before image: http://git.io/vqa6h
# After image: http://git.io/vqaic
from osgeo import gdal, gdal_array
import numpy as np
# "Before" image
im1 = "before.tif"
# "After" image
im2 = "after.tif"
# Load before and after into arrays
ar1 = gdal_array.LoadFile(im1).astype(np.int8)
ar2 = gdal_array.LoadFile(im2)[1].astype(np.int8)
# Perform a simple array difference on the images
diff = ar2 - ar1
# Set up our classification scheme to try
# and isolate significant changes
classes = np.histogram(diff, bins=5)[1]
# The color black is repeated to mask insignificant changes
lut = [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 255, 0], [255, 0, 0]]
# Starting value for classification
start = 1
# Set up the output image
rgb = np.zeros((
3,
diff.shape[0],
diff.shape[1],
), np.int8)
t.up()
t.goto(x - 15, y - 6)
t.write("{}".format((i * label)), align="right")
x_ratio = 709.0 / 256
y_ratio = 450.0 / pixels
colors = ["red", "green", "blue"]
for j in range(len(hist)):
h = hist[j]
x = -354
y = -199
t.up()
t.goto(x, y)
t.down()
t.color(colors[j])
for i in range(256):
x = i * x_ratio
y = h[i] * y_ratio
x = x - (709 / 2)
y = y + -199
t.goto((x, y))
img = "22333.tif"
histograms = []
arr = gdal_array.LoadFile(img)
for b in arr:
histograms.append(histogram(b))
draw_histogram(histograms)
t.pen(shown=False)
t.done
def histogram(a, bins=list(range(0, 256))):
fa = a.flat
n = gdal_array.numpy.searchsorted(gdal_array.numpy.sort(fa), bins)
n = gdal_array.numpy.concatenate([n, [len(fa)]])
hist = n[1:] - n[:-1]
return hist
def stretch(a):
#在gdal_array上传的图像数组中执行直方图均衡化操作
hist = histogram(a)
lut = []
for b in range(0, len(hist), 256):
#步长尺寸
step = reduce(operator.add, hist[b:b + 256]) / 256
#创建均衡的查找表
n = 0
for i in range(256):
lut.append(n / step)
n = n + hist[i + b]
gdal_array.numpy.take(lut, a, out=a)
return a
src = r"D:\Program Files\Python学习文档\samples\FalseColor\Swap.tif"
array = gdal_array.LoadFile(src)
stretched = stretch(array)
output = r"D:\Program Files\Python学习文档\samples\FalseColor\stretched.tif"
output = gdal_array.SaveArray(array, output, format="GTiff", prototype=src)
output = None
Fading = os.path.join('/cygdrive', 'f', 'Fading', 'Tiles', 'Fade')
outdir = os.path.join('/cygdrive', 'f', 'Fading', 'Fade_Merge', 'Netherlands')
rawTiles = os.listdir(bkgd)
rawTiles = [f for f in rawTiles if f.endswith('.tif')]
#closes the raster data set
for bkgdTile in rawTiles:
print bkgdTile
ctTile = os.path.join(country, 'Neth_' + bkgdTile)
fadeIn = os.path.join(Fading, 'FadeIn_' + bkgdTile)
fadeOut = os.path.join(Fading, 'FadeOut_' + bkgdTile)
print ctTile, fadeIn, fadeOut
fadeInArr = gdal_array.LoadFile(fadeIn)
fadeOutArr = gdal_array.LoadFile(fadeOut)
ctArr = gdal_array.LoadFile(ctTile)
bkgdArr = gdal_array.LoadFile(os.path.join(bkgd, bkgdTile))
#get all raster information from sample raster
raster = gdal.Open(ctTile)
ys = raster.RasterYSize #rows
xs = raster.RasterXSize #cols
band = raster.GetRasterBand(1)
ndvC = band.GetNoDataValue()
tr = raster.GetGeoTransform()
pr = raster.GetProjection()
xOrigin = tr[0]
yOrigin = tr[3]
pixelWidth = tr[1]
def img_classify(source, target):
''' img_classify() - Classify a remotely sensed image
Parameters
----------
source : str
imput image file name
target : str
output file name
Returns
-------
nothing
'''
from osgeo import gdal_array as gdal_array
srcArr = gdal_array.LoadFile(source) # Load the image into numpy using gdal
# Split the histogram into 20 bins as our classes
classes = gdal_array.numpy.histogram(srcArr, bins=20)[1]
# Color look-up table (LUT) - must be len(classes)+1, specified as R, G, B tuples
lut = [
[255, 0, 0],
[191, 48, 48],
[166, 0, 0],
[255, 64, 64],
[255, 115, 115],
[255, 116, 0],
[191, 113, 48],
[255, 178, 115],
[0, 153, 153],
[29, 115, 115],
[0, 99, 99],
[166, 75, 0],
[0, 204, 0],
[51, 204, 204],
[255, 150, 64],
[92, 204, 204],
[38, 153, 38],
[0, 133, 0],
[57, 230, 57],
[103, 230, 103],
[184, 138, 0],
]
start = 1 # Starting value for classification
# Set up the RGB color JPEG output image
rgb = gdal_array.numpy.zeros(
(
3,
srcArr.shape[0],
srcArr.shape[1],
),
gdal_array.numpy.float32,
)
for i in range(len(classes)): # Process all classes and assign colors
mask = gdal_array.numpy.logical_and(start <= srcArr, srcArr <= classes[i])
for j in range(len(lut[i])):
rgb[j] = gdal_array.numpy.choose(mask, (rgb[j], lut[i][j]))
start = classes[i] + 1
# Save the image
output = gdal_array.SaveArray(
rgb.astype(gdal_array.numpy.uint8), target, format="JPEG"
)
output = None
return
# else:
# print('No exist nan value in {}'.format(image_name))
# array2raster(image_name, ldata_array,
# xsize=x_size, ysize=y_size,
# transform=trans, projection=proj,
# dtype=gdal.GDT_Float32)
# ldata_list.append(ldata_array)
"""MODIS sacle"""
for path in path_list:
image_name = path.name
#read img as array, method1
#ldata = gdal.Open(str(path))
#ldata_array = ldata.ReadAsArray()
#read img as array, method2
ldata_array = gdal_array.LoadFile(str(path))
ldata_array[ldata_array==0]=np.nan
# replace 0 with nan.
# # two methods to test nan values. method1:
# # if np.isnan(ldata_array).sum>0:
# # two methods to test nan values. method2:
if np.where(np.isnan(ldata_array))[0].size>0:
#np.where(ldata_array==0)#return row and column number of 0 value, start from 0.
print('Exist 0 value in {}, Nan numbers: {}'.format(image_name,np.where(np.isnan(ldata_array))))
ldata_array = pd.DataFrame(ldata_array)
# #filling with mean value of column;https://blog.csdn.net/sinat_29957455/article/details/79017363
ldata_array = ldata_array.fillna(ldata_array.mean())
ldata_array = ldata_array.values
array2raster(image_name, ldata_array,
xsize=x_size, ysize=y_size,
transform=trans, projection=proj,
# Numpy/gdal_array - Read an image, extract a band, save a new image
# https://github.com/GeospatialPython/Learning/raw/master/SatImage.zip
from osgeo import gdal_array
srcArray = gdal_array.LoadFile("SatImage.tif")
band1 = srcArray[0]
gdal_array.SaveArray(band1, "band1.jpg", format="JPEG")
prototype = gdal.Open(prototype)
gdal_array.CopyDatasetInfo(prototype, ds, xoff=xoffset, yoff=yoffset)
return ds
# Multispectral image used
# to create the NDVI. Must
# have red and infrared
# bands
source = "farm.tif"
# Output geotiff file name
target = "ndvi.tif"
# Load the source data as a gdal_array array
srcArray = gdal_array.LoadFile(source)
# Also load as a gdal image to
# get geotransform (world file) info
srcImage = gdal.Open(source)
geoTrans = srcImage.GetGeoTransform()
# Red and infrared (or near infrared) bands
r = srcArray[1]
ir = srcArray[2]
# Clip a field out of the bands using a
# field boundary shapefile
# Create an OGR layer from a Field boundary shapefile
field = ogr.Open("field.shp")
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 4 19:28:54 2020
@author: 立文
"""
#OpenImage.py 使用GDAL库打开栅格数据
from osgeo import gdal
from osgeo import gdal_array
raster = gdal.Open(
r"D:\Program Files\Python学习文档\samples\SatImage\SatImage.tif")
print(raster.RasterCount)
print(raster.RasterXSize)
print(raster.RasterYSize)
srcArray = gdal_array.LoadFile(
r"D:\Program Files\Python学习文档\samples\SatImage\SatImage.tif")
band1 = srcArray[0]
gdal_array.SaveArray(band1,
r"D:\Program Files\Python学习文档\samples\band1.jpg",
format="JPEG")
width=5490,
height=5490,
outputType=gdal.GDT_Int16)
for img in i:
img_file = gdal.Open(img)
gdal.Warp(str(img[0:25]) + '.tif', img_file, options=warp_options)
df = pd.DataFrame(columns=[
'B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09',
'B10', 'B11', 'B12'
])
fileList = glob.glob(img[0:21] + '_B??.tif')
print('writing reflectance values to dataframe...')
for file in fileList:
rasterArray = (gdal_array.LoadFile(file) * 0.0001)
write_band = rasterArray.flatten()
band_number = str(file[-7:-4])
df[band_number] = write_band
print('loading model...')
model = load_model(path_to_model) #path to .h5 model
t3 = time.time()
print('predicting...')
y_pred = model.predict(df.values)
t4 = time.time()
Total = round((t4 - t3) / 60, 1)
print('Predictions complete, took ' + str(Total) + ' minutes.')
print('Writing output raster...')
y_pred_class = np.argmax(y_pred, axis=1)
def loadTifInstance(self, path):
return gdal_array.LoadFile(path)
