def show_tiff(filename):
# open data source.
with rasterio.open(filename) as ds:
# show the data source.
show(ds)
# show the histogram.
show_hist(ds, bins=50, lw=0.0, stacked=False, alpha=0.3, histtype='stepfilled', title="Histogram")
def get_tiff_info(filename):
# open tiff
with rasterio.open(filename) as ds:
# export datatype
print(f'数据格式：{ds.driver}')
# export band counts
print(f'波段数目：{ds.count}')
# export image width
print(f'影像宽度：{ds.width}')
# export image height
print(f'影像高度：{ds.height}')
# export image boundary
print(f'地理范围：{ds.bounds}')
# export reflection transformation parameter
print(f'反射变换参数（六参数模型）：\n {ds.transform}')
# export projection
print(f'投影定义：{ds.crs}')
# show the histogram
show_hist(ds, bins=50, lw=0.0, stacked=False, alpha=0.3, histtype='stepfilled', title='Histogram')
# get the bands counts
num_bands = ds.count
# show the image
for index in range(num_bands):
# define band number.
band = index + 1
# show the gray image.
show((ds, band), title="band" + str(band), cmap='Greys_r')
# show the histogram of every band.
plt.hist(ds.read(band))
# show the title of every band.
plt.title("band" + str(band) + " histogram")
# show.
plt.show()
def polt_histograms(file1, file2):
"""This function outputs the histograms of input datasets."""
try:
fig1, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))
fig1.suptitle(
'Figure 2. Visualization of Resampled dataset and Histogram',
fontsize=18)
fig2, (ax3, ax4) = plt.subplots(1, 2, figsize=(14, 7))
with rasterio.open(file1, 'r') as src1:
plot.show((src1, 1), cmap='Greys_r', interpolation='none', ax=ax1)
ax1.set_title('Resample(2x).tif', fontsize=16)
ax1.set_xlabel('X /metre', fontsize=12)
ax1.set_ylabel('Y /metre', fontsize=12)
plot.show_hist(src1, ax=ax3)
ax3.set_title('Histogram of Resample(2x).tif', fontsize=16)
ax3.set_xlim(0, 1600)
with rasterio.open(file2, 'r') as src2:
plot.show((src2, 1), cmap='Greys_r', interpolation='none', ax=ax2)
ax2.set_title('Resample(4x).tif', fontsize=16)
ax2.set_xlabel('X /metre', fontsize=12)
ax2.set_ylabel('Y /metre', fontsize=12)
plot.show_hist(src2, ax=ax4)
ax4.set_title('Histogram of Resample(4x).tif', fontsize=16)
ax4.set_xlim(0, 1600)
plt.show()
except rasterio.errors.RasterioIOError as e:
print('Wrong input file: ' + str(e))
def getTIFFInfo(imagepath):
with rasterio.open(imagepath) as ds:
print(f'数据格式：{ds.driver}')
print(f'波段数目：{ds.count}')
print(f'影像宽度：{ds.width}')
print(f'影像高度：{ds.height}')
print(f'地理范围：{ds.bounds}')
print(f'反射变换参数（六参数模型）：\n {ds.transform}')
print(f'投影定义：{ds.crs}')
show_hist(ds,
bins=50,
lw=0.0,
stacked=False,
alpha=0.3,
histtype='stepfilled',
title="Histogram")
num_bands = ds.count
print('波段数为：' + str(num_bands))
for index in range(num_bands):
band = index + 1
show((ds, band), cmap='Greys_r')
plt.hist(ds.read(band))
plt.title("band" + str(band))
plt.show()
def showTiFF(imagepath):
with rasterio.open(imagepath) as ds:
show(ds)
show_hist(ds,
bins=50,
lw=0.0,
stacked=False,
alpha=0.3,
histtype='stepfilled',
title="Histogram")
def test_show_hist_large():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
try:
rand_arr = np.random.randn(10, 718, 791)
show_hist(rand_arr)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_hist_large():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
try:
rand_arr = np.random.randn(10, 718, 791)
show_hist(rand_arr)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def histogram(self, ax=None, **kwargs):
"""
Method to plot a histogram of digital numbers
Parameters
----------
ax : matplotlib.pyplot.axes
optional matplotlib axes for plotting
**kwargs :
matplotlib keyword arguments
see matplotlib documentation for valid
arguments for histogram
Returns
-------
ax : matplotlib.pyplot.axes
"""
if rasterio is None:
msg = (
"Raster().histogram(): error "
+ 'importing rasterio - try "pip install rasterio"'
)
raise ImportError(msg)
else:
from rasterio.plot import show_hist
if "alpha" not in kwargs:
kwargs["alpha"] = 0.3
if self._dataset is not None:
ax = show_hist(self._dataset, ax=ax, **kwargs)
else:
d0 = len(self.__arr_dict)
d1, d2 = None, None
for _, arr in self.__arr_dict.items():
d1, d2 = arr.shape
if d1 is None:
raise AssertionError("No plottable arrays found")
data = np.zeros((d0, d1, d2), dtype=float)
i = 0
for _, arr in sorted(self.__arr_dict.items()):
data[i, :, :] = arr
i += 1
data = np.ma.masked_where(data == self.nodatavals, data)
ax = show_hist(data, ax=ax, **kwargs)
return ax
def get_histogram(document_id):
raster = read_document(document_id)
plt.show = lambda: None
show_hist(raster,
title='Histogram',
histtype='stepfilled',
bins=50,
lw=0.0,
stacked=False,
alpha=0.3)
return get_image_from_plot()
def test_show_hist_mplargs():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show_hist(src, bins=50, lw=0.0, stacked=False, alpha=0.3,
histtype='stepfilled', title="World Histogram overlaid")
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def raster_histogram(input, varname):
"""Create raster histograms each band in a raster and save them to file"""
# Read the data
raster = rasterio.open(input)
for i in raster.indexes:
fig, ax = plt.subplots(1)
# TODO: Make this calculation automatic based on SD or something
ax.set_xlim([-0.5, 0.5])
show_hist(raster.read(i, masked=True), bins=50, lw=2, masked=True, alpha=0.6,
title="Histogram - band{}".format(i), ax=ax, facecolor='blue')
plt.xlabel(varname)
fig.savefig(varname + "_hist_band{}".format(i), dpi=300, bbox_inches='tight')
def test_show_hist_mplargs():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
matplotlib = pytest.importorskip('matplotlib')
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show_hist(src, bins=50, lw=0.0, stacked=False, alpha=0.3,
histtype='stepfilled', title="World Histogram overlaid")
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def image_histogram(ndwi2):
from rasterio.plot import show_hist
co, ce =show_hist(ndwi)
fig = plt.figure(figsize=(10,7))
fig.set_facecolor('yellow')
plt.plot(ce[1::], co[1::])
plt.show()
def calculate_ndwi(rasterfile, outfile=None, plot=False):
raster_filepath = os.path.dirname(rasterfile) + "/"
raster_filename = os.path.basename(rasterfile)
img = raster_filepath + raster_filename
print("Opening", raster_filename, "to read in band data:", end=" ")
with rasterio.open(img, driver="GTiff") as src:
kwargs = src.meta
kwargs.update(dtype=rasterio.float32, count=1)
nir_num = src.count # adjusting NIR band to 4 or 5 band images
green_band = src.read(2).astype(rasterio.float32) # band 2 - green
nir_band = src.read(nir_num).astype(rasterio.float32) # band 4 - NIR
print("DONE\n")
print("Calculating NDWI:", end=" ")
np.seterr(divide='ignore', invalid='ignore')
ndwi = (green_band - nir_band) / (green_band + nir_band)
print("DONE\n")
ndwi = cv2.GaussianBlur(ndwi, (17, 17), 0)
if outfile:
out_filename = outfile
else:
out_filename = raster_filepath + raster_filename.split(
sep=".")[0] + "_NDWI.tif"
print("Saving calculated NDWI image as", out_filename, ":", end=" ")
with rasterio.open(out_filename, 'w', **kwargs) as dst:
dst.nodata = 0
dst.write_band(1, ndwi.astype(rasterio.float32))
print("DONE\n")
if plot:
bands = [ndwi]
labels = ["NDWI (Normalized Difference Water Index)"]
plot_raster(bands, labels)
show_hist(ndwi,
bins=100,
stacked=False,
alpha=0.3,
histtype='stepfilled',
title="NDWI Values")
return out_filename
def make_nice_single_raster_plot(input_raster, no_of_histg_bins):
fig, (ax1, ax2, ax3) = pyplot.subplots(1, 3, figsize=(21, 7))
show(input_raster.read(),
ax=ax1,
transform=input_raster.transform,
cmap='BrBG',
title='#NoRainbow')
show(input_raster.read(),
ax=ax2,
transform=input_raster.transform,
contour=True,
title='contours')
show_hist(input_raster,
bins=no_of_histg_bins,
lw=0.0,
stacked=False,
histtype='stepfilled',
title="Histogram of height vals")
def test_show_hist():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show_hist((src, 1), bins=256)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
try:
show_hist(src.read(), bins=256)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def test_show_hist():
"""
This test only verifies that code up to the point of plotting with
matplotlib works correctly. Tests do not exercise matplotlib.
"""
with rasterio.open('tests/data/RGB.byte.tif') as src:
try:
show_hist((src, 1), bins=256)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
try:
show_hist(src.read(), bins=256)
fig = plt.gcf()
plt.close(fig)
except ImportError:
pass
def display_img(self, path):
## matplob
#im = plt.imshow(b)
#plt.colorbar(im)
#plt.show()
## rasterio
src = rasterio.open(path + "/htAccumulation.tif")
plt.imshow(src.read(1), cmap='pink')
plt.show()
show_hist(src,
bins=50,
lw=0.0,
stacked=False,
alpha=0.3,
histtype='stepfilled',
title="Histogram")
def png_hist(img_pth, no):
fold_loc, title = split(img_pth)
save_name = title[:-4]
img_type = save_name.split("_")
img_type = img_type[-1]
# Determine bins
bins = np.arange(no + 2) - 0.5
# Open raster
with rasterio.open(img_pth) as src:
fig, (ax1) = plt.subplots(1, 1, figsize=(7, 7))
show_hist(src, bins=bins, lw=0.0, title=title, ax=ax1)
plt.xticks(np.arange(0, no + 2, 1))
plt.legend([img_type])
save_pth = fold_loc + "\\" + save_name + "_hist.png"
plt.savefig(save_pth)
# plt.show()
return save_pth
def plot_slope_distribution(slope_1, slope_2, algorithms):
"""This fuction plots the distribution of the slope values
for the input algorithms."""
try:
if type(algorithms) != str:
raise ValueError
else:
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 7))
fig.suptitle(algorithms, fontsize=18)
plot.show_hist(slope_1, bins=50, ax=ax1)
ax1.set_title('Slope Value Distribution of Resample(2x).tif',
fontsize=16)
ax1.set_xlim(0, 90)
plot.show_hist(slope_2, bins=50, ax=ax2)
ax2.set_title('Slope Value Distribution of Resample(4x).tif',
fontsize=16)
ax2.set_xlim(0, 90)
plt.show()
except ValueError:
print('Input parameter(algorithms) must be str!')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('source_dem', metavar='source_dem', type=str, help='source_dem path')
parser.add_argument('factor', metavar='factor', type=int, help='dec factor')
args = parser.parse_args()
source_dem = args.source_dem
factor = args.factor
dem = source_dem.split('/')[-1]
path = source_dem.replace(dem,'')
output_path = path + '{}_{}.tif'
# Resample the source image using different resampling methods
resample_list = ['nearest','average','bilinear','gauss','cubic','cubicspline','average_magphase','mode']
dec_factor = str(factor)
for resample in resample_list:
print("gdaladdo -r "+ resample +" -ro " + source_dem + " --config COMPRESS_OVERVIEW LZW " + dec_factor)
os.system("gdaladdo -r "+ resample +" -ro " + source_dem + " --config COMPRESS_OVERVIEW LZW " + dec_factor)
ovr = '.ovr'
ovr_path = r'{}{}'.format(source_dem,ovr)
new_path = output_path.format(resample,dec_factor)
os.rename(ovr_path,new_path)
# Read each resampled image an then:
# extract the information
# create a thumbnail and histogram
for filename in glob.glob(os.path.join(path, '*.tif')):
with open(os.path.join(os.getcwd(), filename), 'r') as f: # open in readonly mode
cog_sample = rasterio.open(filename)
cur_name = os.path.basename(filename)[:-4]
bound = str(cog_sample.bounds)
crs = str(cog_sample.crs)
dtype = str(cog_sample.dtypes[0])
trans = str(cog_sample.transform)
nodata = str(cog_sample.nodata)
shape = str(cog_sample.shape)
meta = str(cog_sample.meta)
output = "{}_info".format(cur_name)
f = open(output+'.txt',"w")
f.write("stats: " + "\n" +
"bound: " + bound + "\n" +
"crs: " + crs + "\n" +
"dtype: " + dtype +
"transform: " + trans +
"nodata: " + nodata +
"shape: " + shape +
"metadata: " + meta)
fig, (aximage, axhist) = pyplot.subplots(1, 2, figsize=(49,28))
figtitle="{} sample cog and histogram".format(cur_name)
fig.suptitle(figtitle,fontsize=30)
#create histogram using rasterio show histogram, pass axes subplot handle to axhist
bins=50
show_hist(cog_sample, bins=bins, lw=0.0,
stacked=False, alpha=0.3,
histtype='stepfilled',
ax=axhist)
axhist.set_title('')
axhist.set_xlabel('elevation (m)')
axhist.set_ylabel('')
axhist.legend('')
#create display image passing axes subplot handle to aximage
show(cog_sample, cmap='gray',transform=cog_sample.transform, ax=aximage)
#save the figure
fname=path+cur_name+'.png'
pyplot.savefig(fname,format='png')
fields = ['Name', 'Resampling_Method', 'Overview', 'Driver', 'Data Type',
'crs', 'Width', 'Height', 'Nodata_Pixels', 'Pixels_with_Values',
'Pixel_Size', 'Min', 'Max', 'Range', 'Q1', 'Q3', 'Mean', 'Median',
'Std', 'Var']
rows = [fields]
for filename in glob.glob(os.path.join(path, '*.tif')):
with open(os.path.join(os.getcwd(), filename), 'r') as f: # open in readonly mode
cog = rasterio.open(filename)
cur_name = os.path.basename(filename).split('_')[0]
resampling = os.path.basename(filename)[:-7]
overview = 32
# delete nodata value
cog_ar = cog.read(1)
cog_ar = numpy.where(cog_ar==cog.nodata,numpy.nan,cog_ar)
# stats
driver = str(cog.meta['driver'])
dtype = str(cog.meta['dtype'])
crs = str(cog.meta['crs'])
width = cog.meta['width']
height = cog.meta['height']
nodata = len(cog_ar[numpy.isnan(cog_ar)])
data_with_values = len(cog_ar[~numpy.isnan(cog_ar)])
pixel_size = cog.transform[0]
cog_min = numpy.nanmin(cog_ar)
cog_max = numpy.nanmax(cog_ar)
cog_range = cog_max-cog_min
cog_q1 = numpy.nanquantile(cog_ar, 0.25)
cog_q3 = numpy.nanquantile(cog_ar, 0.75)
cog_mean = numpy.nanmean(cog_ar)
cog_median = numpy.nanmedian(cog_ar)
cog_std = numpy.nanstd(cog_ar)
cog_var = numpy.nanvar(cog_ar)
cur_row = [cur_name,
resampling,
overview,
driver,
dtype,
crs,
width,
height,
nodata,
data_with_values,
pixel_size,
cog_min,
cog_max,
cog_range,
cog_q1,
cog_q3,
cog_mean,
cog_median,
cog_std,
cog_var]
rows.append(cur_row)
# name of csv file
opath = path+'stats_{}.csv'
output = opath.format(cur_name)
# writing to csv file
with open(output, 'w',newline='') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(fields)
csvwriter.writerow(cur_row)
# name of csv file
output = "{}/stats_all.csv".format(path)
# writing to csv file
with open(output, 'w',newline='') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerows(rows)
def show_hist(self, bands=1, bins=50, **kwargs):
show_hist(self.read(bands), bins=bins, **kwargs)
# Empty pixel https://mapbox.github.io/rasterio/topics/masks.html
# Calculate the stats for all channels
stats = []
# Read all bands at one go
bands = raster.read()
for band in bands:
stats.append({
'min': band.min(),
'max': band.max(),
'median': np.median(band), #median on np(numpyssa), muut band
'mean': band.mean()
})
stats
# Visualize near infrared
show(nir, cmap='terrain')
# Visualize the histogram
from rasterio.plot import show_hist
show_hist(raster,
bins=50,
lw=0.0,
stacked=False,
alpha=0.3,
histtype='stepfilled',
title='Histogram')
import rasterio
from rasterio.plot import show_hist
import numpy as np
path = r"F:\WORDWIDE_Resample\Merge_predict_ssw\Merge\Union/"
from scipy import stats
with rasterio.open(path + 'Union201604.tif') as src:
array = src.read(1)
show_hist(array,
bins=50,
lw=0.0,
stacked=False,
alpha=0.3,
histtype='stepfilled',
title="Histogram")
array = array.flatten()
array = np.delete(array, np.where(array == 0), None)
print(stats.normaltest(array))
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import pandas
from scipy.stats import norm
x = array
print(x)
mu = np.mean(x) #计算均值
sigma = np.std(x)
"width": out_image.shape[2],
"transform": out_transform})
with rio.open("RGB_sopo_big_masked.tif", "w", **out_meta) as dest:
dest.write(out_image)
rgb_plt = rio.open('RGB_sopo_big_masked.tif')
fig = plt.figure(figsize=(16,16))
plt.imshow(rgb_plt.read(1), cmap='pink')
#subplot histogram and image !!!!!
from rasterio.plot import show_hist
fig, (axrgb, axhist) = plt.subplots(1, 2, figsize=(14,7))
show(rgb_plt, ax=axrgb)
show_hist(rgb_plt, bins=50, histtype='stepfilled',
lw=0.0, stacked=False, alpha=0.3, ax=axhist)
plt.show()
with rio.open(R10+'T18NXL_20200109T152631_B04.jp2') as src:
out_image, out_transform = rio.mask.mask(src, sopo_proj.geometry,crop=True)
out_meta = src.meta.copy()
out_meta.update({"driver": "GTiff",
"height": out_image.shape[1],
"width": out_image.shape[2],
"transform": out_transform})
with rio.open("RGB_big_red.tif", "w", **out_meta) as dest:
dest.write(out_image)
with rio.open(R10+'T18NXL_20200109T152631_B08.jp2') as src:
def main():
print("start")
print(datetime.now())
###### Test cases different dem tiles url and appropriate bbox in lon,lat EPSG:4326
## # define the select window longitude and latitude
#### url="http://datacube-prod-data-public.s3.ca-central-1.amazonaws.com/store/dem/cdem/cdem-25-dem.tif"
#### miny=-64.7
#### maxy=-64.8
#### minx=46.5
#### maxx=46.6
####
#### # JF's original coordinates (was not completed after 15 minutes on country internet)
#### url="http://datacube-prod-data-public.s3.ca-central-1.amazonaws.com/store/dem/cdem/cdem-11-dem.tif"
#### miny=-63.5
#### maxy=-68.5
#### minx=63.25
#### maxx=68.25
#### # Norah had to use a smaller size (download took aprox 8 minutes on country internet)
#### miny=-68.5
#### maxy=-66.5
#### minx=66.25
#### maxx=68.25
##
## #http://datacube-prod-data-public.s3.ca-central-1.amazonaws.com/store/dem/cdem/cdem-7-dem.tif
## miny=-139.5
## maxy=-137.5
## minx=60.0
## maxx=62.0
## ## open cog by url
##
## url="http://datacube-prod-data-public.s3.ca-central-1.amazonaws.com/store/dem/cdem/cdem-7-dem.tif"
## file_name="./sample-%s"%(url.split('/')[-1])
## # open witn all types of GeoTIFF georeference except that within the TIFF file’s keys and tags turned off
##
## cog=rasterio.open(url,GEOREF_SOURCES='INTERNAL')
## #cog bounds and crs
## print(cog.bounds)
## print(cog.crs)
## print(cog.dtypes[0])
## print(cog.transform)
## print(cog.nodata)
## print(cog.shape)
## print(cog.meta)
## print(cog.block_shapes)
## #reproject the window in the cogs projection
## llbox=box(miny,minx,maxy,maxx)
## coords_window=shape(transform_geom('EPSG:4326',cog.crs,mapping(llbox)))
## # select window bounds as var
## wb=coords_window.bounds
## print(wb)
## # create the sample window from the bounds using the cogs (rasterio) window method
## sample_window=cog.window(wb[0],wb[1],wb[2],wb[3])
## print(sample_window)
## # read the sample from the image,
## # underlying gdal functionality may resample using nearest neighbour
## # based on your download speed this could take a while
## sample_cog=cog.read(1,window=sample_window)
## print(sample_cog)
## # write the sample out to a file 'sample_<filename>'
## with rasterio.open(
## file_name,
## 'w',
## driver='GTiff',
## height=sample_cog.shape[0],
## width=sample_cog.shape[1],
## count=1,
## dtype=cog.dtypes[0],
## crs=cog.crs,
## transform=cog.transform,
## nodata=cog.nodata,
## GEOREF_SOURCES='INTERNAL'
## ) as dst:
## dst.write(sample_cog, 1)
## dst.close()
## cog.close()
###################### getting histogram statistics using rasterio, matplotlib and numpy
import rasterio
from rasterio.plot import show_hist, show
from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D
import numpy
# if using all code from above pass file name to sample, if not then define sample
##sample=file_name
sample = 'C:\\users\\nbrown\\documents\\datacube\\stage\\example-code\\sample-cdem-7-dem.tif'
cog_sample = rasterio.open(sample)
print("bounds: %s" % (str(cog_sample.bounds)))
print("crs: %s" % (cog_sample.crs))
print("data type: %s" % (str(cog_sample.dtypes[0])))
print("transform: %s" % str((cog_sample.transform)))
print("pixel resolution from transform: %s" %
(str(cog_sample.transform[0])))
print("no data: %s" % (str(cog_sample.nodata)))
print("image size in pixes: %s" % (str(cog_sample.shape)))
print("cog header info: %s" % (cog_sample.meta))
#make a figure with two axes subplot
fig, (aximage, axhist) = pyplot.subplots(1, 2, figsize=(7, 4))
figtitle = "original sample cog and histogram"
fig.suptitle(figtitle, fontsize=10)
#create histogram using rasterio show histogram, pass axes subplot handle to axhist
bins = 50
show_hist(cog_sample,
bins=bins,
lw=0.0,
stacked=False,
alpha=0.3,
histtype='stepfilled',
ax=axhist)
axhist.set_title('')
axhist.set_xlabel('elevation (m)')
axhist.set_ylabel('')
axhist.legend('')
#create display image passing axes subplot handle to aximage
show(cog_sample, cmap='gray', transform=cog_sample.transform, ax=aximage)
#save the figure
fname = './im-hist-test.png'
pyplot.savefig(fname, format='png')
#plot the figure
## pyplot.show()
#nmupy array gives lots of option
cog_sample = rasterio.open(sample)
#read cog into numpy array
cog_ar = cog_sample.read(1)
##### 3D plots start
# create a 3d axes
fig = pyplot.figure(figsize=(7, 4))
fig.suptitle("3d elevation (m)", fontsize=10)
ax = fig.add_subplot(111, projection='3d')
#create a 1D array for x /columns / longitude
x = numpy.arange(cog_ar.shape[1])
#create a 1D array for y/ rows / lattitude
y = numpy.arange(cog_ar.shape[0])
# merge two 1D arrays in a 2d xy array
X, Y = numpy.meshgrid(x, y)
# replace no data value with 'not a number'
# could also try empty
cog_ar = numpy.where(cog_ar == cog_sample.nodata, numpy.nan, cog_ar)
cog_ar = numpy.where(cog_ar == cog_sample.nodata, numpy.nan, cog_ar)
# wireframe handles nan without warning
#ax.plot_wireframe(X,Y,cog_ar)
ax.plot_surface(X, Y, cog_ar)
# save plots to file
pyplot.savefig('./test3d.png', format='png')
#show plot
pyplot.show()
#### 3D plots end
#get stats from array
#min
print(numpy.amin(cog_ar))
#max
print(numpy.amax(cog_ar))
# plot the image
#pyplot.show()
#close image read
cog_sample.close()
print("finished")
print(datetime.now())
def relatorio_metadados(banda):
print('Buscando os metadados...')
print('')
print('')
print(f'O nome do arquivo é: {banda.name}')
print('')
print(f'As dimensões são: {banda.width} x {banda.height} pixels;')
print('')
print(f'O tipo de dado é: {banda.dtypes}')
print('')
print(f'O sistema de referência de coordenadas é: {banda.crs}')
print('')
print(f'As coordenadas de canto são: {banda.bounds}')
print('')
print('')
# Plotagem da imagem da banda
print('Plotando a imagem...')
print('')
if banda == B_azul:
plot.show(banda, cmap='Blues', title='Banda do azul')
elif banda == B_verde:
plot.show(banda, cmap='Greens', title='Banda do verde')
elif banda == B_vermelho:
plot.show(banda, cmap='Reds', title='Banda do vermelho')
elif banda == B_NIR:
plot.show(banda,
cmap='Oranges',
title='Banda do infravermelho próximo')
else:
pass
print('Imagem plotada com sucesso!')
print('')
# Plotagem do histograma
print('Plotando o histograma...')
print('')
if banda == B_azul:
plot.show_hist(banda,
bins=100,
lw=0.0,
alpha=0.5,
title='Histograma - Banda do Azul')
elif banda == B_verde:
plot.show_hist(banda,
bins=100,
lw=0.0,
alpha=0.5,
title='Histograma - Banda do Verde')
elif banda == B_vermelho:
plot.show_hist(banda,
bins=100,
lw=0.0,
alpha=0.5,
title='Histograma - Banda do Vermelho')
elif banda == B_NIR:
plot.show_hist(banda,
bins=100,
lw=0.0,
alpha=0.5,
title='Histograma - Banda do Infravermelho Próximo')
else:
pass
print('Histograma plotado com sucesso!')
print('')
return
from rasterio.plot import show, show_hist
from projections.atlas import atlas
from projections.rasterset import RasterSet, Raster
from projections.simpleexpr import SimpleExpr
import projections.predicts as predicts
import projections.rds as rds
# Import standard PREDICTS rasters
rasters = predicts.rasterset('rcp', 'aim', 2020, 'medium')
rs = RasterSet(rasters)
rs['logHPD_rs2'] = SimpleExpr('logHPD_rs', 'scale(log(hpd + 1), 0.0, 1.0)')
data1 = rs.eval('logHPD_rs')
data2 = rs.eval('logHPD_rs2')
data3 = np.where(np.isclose(data1, data2, equal_nan=True), 1, 0)
diff = np.fabs(data1 - data2)
print("max diff %f" % diff.max())
print("max in hpd_ref: %f" % rs['hpd_ref'].data.values.max())
print("max in hpd: %f" % rs['hpd'].data.dropna().values.max())
fig, ((ax1, ax2, ax3), (hx1, hx2, hx3)) = plt.subplots(2, 3, figsize=(21, 7))
show(data1, ax=ax1, cmap='Greens', title='Global max/min')
show(data2, ax=ax2, cmap='Greens', title='Computed max/min')
show(diff, ax=ax3, cmap='viridis', title='Difference', vmin=0, vmax=1.0)
show_hist(data1, ax=hx1, histtype='stepfilled')
show_hist(data2, ax=hx2, histtype='stepfilled')
show_hist(diff, ax=hx3, histtype='stepfilled')
plt.show()
show((raster, 1), cmap="Blues", ax=ax3)
# Set titles
ax1.set_title("Red")
ax2.set_title("Green")
ax3.set_title("Blue")
# Create true color composite
#Read channels into numpy arrays
red = raster.read(3)
green = raster.read(2)
blue = raster.read(1)
# Normalize the bands. This function was imported, so you don't need to write the code several times
# And if you have a mistake you need to correct it only once
redn = normalize(red)
greenn = normalize(green)
bluen = normalize(blue)
# Create the composite
rgb = np.stack((redn, greenn, bluen))
array
#Plot the RGB image (THIS doesn't work)
plt.imshow(rgb)
# Plot histogram of the data
show_hist(raster, bins=50, lw=0.0, stacked=False, alpha=0.3, histtype="stepfilled", title="Histogram of Landsat")
ply
#clip the raster with rasterio mask
for i in range(len(ply)):
out_img, out_transform = mask(raster=rst[i], shapes=ply[i], crop=True)
out_meta = rst[i].meta.copy()
print(out_meta)
#update metadata
epsg_code = int(32618) #http://www.spatialreference.org/ref/epsg/wgs-84-utm-zone-18n/
print(epsg_code)
out_meta.update({"driver": "GTiff",
"height": out_img.shape[1],
"width": out_img.shape[2],
"transform": out_transform,
#"crs": pycrs.parser.from_epsg_code(epsg_code).to_proj4()}
})
out_file = (out_path + out_name[i])
with rasterio.open((out_file), "w", **out_meta) as dest:
dest.write(out_img)
dest.close()
#check the file by plotting map and histogram
for i in range(len(ply)):
out_file = (out_path + out_name[i])
clipped = rasterio.open(out_file)
show((clipped), cmap='terrain')
show_hist(clipped)
band_red = rasterio.open('LE07_L1TP_044034_20021222_20160927_01_T1_B3.tif')
band_nir = rasterio.open('LE07_L1TP_044034_20021222_20160927_01_T1_B4.tif')
red = band_red.read(1).astype('float64')
nir = band_nir.read(1).astype('float64')
ndvi = np.where((nir + red) == 0., 0, (nir - red) / (nir + red))
ndviImage = rasterio.open('NDVI_RASTERIO.jpg',
'w',
driver='Gtiff',
width=band_red.width,
height=band_red.height,
count=1,
crs=band_red.crs,
transform=band_red.transform,
dtype='float64')
ndviImage.write(ndvi, 1)
ndviImage.close()
ndvi = rasterio.open('NDVI_RASTERIO.jpg')
plot.show(ndvi, cmap='viridis', title='NDVI')
#plot.show(ndvi,cmap='RdYlGn',title='NDVI')
plot.show_hist(ndvi,
bins=50,
lw=0.0,
stacked=False,
alpha=0.3,
histtype='stepfilled',
title="Histogram")
plt.show()
from rasterio.plot import show
fig, (axdsm, axdtm, axchm) = plt.subplots(1, 3, figsize=(21, 7))
show(DSM, ax=axdsm, title='DSM')
show(DTM, ax=axdtm, title='DTM')
show(CHM, ax=axchm, title='CHM')
plt.show()
#plt.savefig('./outputimages/DSMDTMCHMImage.png')
from rasterio.plot import show_hist
fig, (axdsm, axdtm, axchm) = plt.subplots(1, 3, figsize=(21, 7))
show_hist(DSM,
ax=axdsm,
bins=100,
lw=0.0,
stacked=False,
alpha=0.3,
title="Histogram DSM")
show_hist(DTM,
ax=axdtm,
bins=100,
lw=0.0,
stacked=False,
alpha=0.3,
title="Histogram DTM")
show_hist(CHM,
ax=axchm,
bins=100,
lw=0.0,
stacked=False,
red_normalised.mean())
print(green_normalised.min(), '-', green_normalised.max(), 'mean:',
green_normalised.mean())
print(blue_normalised.min(), '-', blue_normalised.max(), 'mean:',
blue_normalised.mean())
# Create RGB natural color composite
rgb = np.dstack((red_normalised, green_normalised, blue_normalised))
# Let's see how our color composite looks like
plt.imshow(rgb)
#Read and normalise NIR band
nir = raster.read(4)
nir_normalised = normalize(nir)
#Create the composite stacking
nrg = np.dstack((nir_normalised, red_normalised, green_normalised))
#Show the composite
plt.imshow(nrg)
#Show histogram
show_hist(raster,
bins=50,
lw=0.0,
stacked=False,
alpha=0.3,
histtype='stepfilled',
title="Histamiini")
out_img, out_transform = mask(raster=data, shapes=coords, crop=True)
# Copy the metadata
out_meta = data.meta.copy()
# Parse EPSG code
epsg_code = int(data.crs.data['init'][5:])
# Write the clipped raster to disk
out_meta.update({
"driver": "GTiff",
"height": out_img.shape[1],
"width": out_img.shape[2],
"transform": out_transform,
"crs": pycrs.parser.from_epsg_code(epsg_code).to_proj4()
})
with rasterio.open(out_tif, "w", **out_meta) as dest:
dest.write(out_img)
clipped = rasterio.open(out_tif)
show((clipped, 5))
# Show histogram for the clipped region
show_hist(clipped,
bins=70,
lw=0.0,
stacked=False,
alpha=0.3,
histtype='stepfilled',
title="Histogram")