def plot_RGB(geotif, rgb_bands=[1,2,3], file_extension='jpg', dpi=150, figsize=(10,10)):
"""
Calculates a RGB image (True color composite) based on red, greed, and blue bands.
:param geotif: geotif file containning one band with NDVI values
:param file_extension: format of the output graphic. default='png'
:param rgb_bands: order of bands storing red, green and blue values default=[1,2,3]
:result str: path to graphic file
"""
from numpy import dstack
gdal.UseExceptions()
ds = gdal.Open(geotif)
data = ds.ReadAsArray()
gt = ds.GetGeoTransform()
proj = ds.GetProjection()
inproj = osr.SpatialReference()
inproj.ImportFromWkt(proj)
projcs = inproj.GetAuthorityCode('PROJCS')
projection = ccrs.epsg(projcs)
# print(projection)
subplot_kw = dict(projection=projection)
fig, ax = plt.subplots( subplot_kw=subplot_kw)
extent = (gt[0], gt[0] + ds.RasterXSize * gt[1],
gt[3] + ds.RasterYSize * gt[5], gt[3])
red = ds.GetRasterBand(rgb_bands[0])
green = ds.GetRasterBand(rgb_bands[1])
blue = ds.GetRasterBand(rgb_bands[2]) # band 1 PSSCINE4Band blue
img_r = red.ReadAsArray(0, 0, ds.RasterXSize, ds.RasterYSize)
img_g = green.ReadAsArray(0, 0, ds.RasterXSize, ds.RasterYSize)
img_b = blue.ReadAsArray(0, 0, ds.RasterXSize, ds.RasterYSize)
# rgb = dstack((data[0, :, :], data[1, :, :], data[2, :, :]))
rgb = dstack([img_r, img_g, img_b])
img = ax.imshow(rgb, extent=extent, origin='upper', transform=projection)
# img = ax.imshow(rgb.transpose((1, 2, 0)), extent=extent,
# origin='upper')
ax.gridlines(color='lightgrey', linestyle='-')
# ax.set_xticks()
tcc_plot = vs.fig2plot(fig, dpi=dpi, figsize=figsize, file_extension='jpg')
plt.close()
ds = None
return tcc_plot
def _handler(self, request, response):
from tempfile import mkstemp
tic = dt.now()
init_process_logger('log.txt')
response.outputs['output_log'].file = 'log.txt'
LOGGER.info('Start process')
response.update_status('Execution started at : {}'.format(tic), 1)
######################################
# Read inputs
######################################
try:
resource = archiveextract(
resource=rename_complexinputs(request.inputs['resource']))[0]
fmts = [e.data for e in request.inputs['fmt']]
title = request.inputs['title'][0].data
except Exception as e:
msg = 'Failed to read input parameter {}'.format(e)
LOGGER.error(msg)
raise Exception(msg)
response.update_status('Input parameters ingested', 2)
try:
fig = map_spatial_analog(resource, title=title)
output = []
for fmt in fmts:
output.append(fig2plot(fig, fmt))
except Exception as e:
msg = "Failed to create figure: {}".format(e)
LOGGER.error(msg)
raise Exception(msg)
finally:
plt.close()
if len(fmts) == 1:
output = output[0]
else:
output = archive(output)
response.outputs['output_figure'].file = output
response.update_status("done", 100)
return response
def plot_products(products, extend=[10, 20, 5, 15]):
"""
plot the products extends of the search result
:param products: output of sentinel api search
:return graphic: map of extents
"""
import numpy as np
from matplotlib.patches import Polygon
import matplotlib.patches as mpatches
from matplotlib.collections import PatchCollection
from cartopy import config as cartopy_config
import cartopy.feature as cfeature
from cartopy.util import add_cyclic_point
import re
fig = plt.figure(dpi=90, facecolor='w', edgecolor='k')
projection = ccrs.PlateCarree()
ax = plt.axes(projection=projection)
ax.set_extent(extend)
ax.stock_img()
ax.coastlines()
ax.add_feature(cfeature.BORDERS)
pat = re.compile(r'''(-*\d+\.\d+ -*\d+\.\d+);*''')
for key in products.keys():
polygon = str(products[key]['footprint'])
# s = 'POLYGON ((15.71888453311329 9.045763865974665,15.7018748825589 8.97110837227606,15.66795226563288 8.822558900399137,15.639498612331632 8.69721920092792,15.63428409805786 8.674303514900869,15.600477269179995 8.525798537094156,15.566734239298787 8.377334323160321,15.53315342410745 8.228822837291709,15.499521168391912 8.080353481086165,15.493321895031096 8.052970059354971,14.999818486685434 8.053569047879877,14.999818016115439 9.046743365203026,15.71888453311329 9.045763865974665))'
matches = pat.findall(polygon)
if matches:
xy = np.array([map(float, m.split()) for m in matches])
ax.add_patch(mpatches.Polygon(xy, closed=True, transform=ccrs.PlateCarree(), alpha=0.4)) # color='coral'
# ccrs.Geodetic()
ax.gridlines(draw_labels=True,)
img = vs.fig2plot(fig, output_dir='.')
return img
def plot_ndvi(geotif, file_extension='jpg', dpi=150, figsize=(10,10)):
"""
plots a NDVI image
:param geotif: geotif file containning one band with NDVI values
:param file_extension: format of the output graphic. default='png'
:result str: path to graphic file
"""
# https://ocefpaf.github.io/python4oceanographers/blog/2015/03/02/geotiff/
gdal.UseExceptions()
norm = vs.MidpointNormalize(midpoint=0)
ds = gdal.Open(geotif)
gt = ds.GetGeoTransform()
proj = ds.GetProjection()
inproj = osr.SpatialReference()
inproj.ImportFromWkt(proj)
projcs = inproj.GetAuthorityCode('PROJCS')
projection = ccrs.epsg(projcs)
# print("Projection: %s " % projection)
subplot_kw = dict(projection=projection)
fig, ax = plt.subplots( subplot_kw=subplot_kw)
extent = (gt[0], gt[0] + ds.RasterXSize * gt[1],
gt[3] + ds.RasterYSize * gt[5], gt[3])
bnd1 = ds.GetRasterBand(1)
data = bnd1.ReadAsArray(0, 0, ds.RasterXSize, ds.RasterYSize) # buf_xsize=ds.RasterXSize/10, buf_ysize=ds.RasterYSize/10,
img_ndvi = ax.imshow(data, extent=extent,origin='upper', norm=norm, vmin=-1, vmax=1, cmap=plt.cm.BrBG, transform=projection)
# img_ndvi = ax.imshow(data, extent=extent, # [:3, :, :].transpose((1, 2, 0))
# origin='upper',norm=norm, vmin=-1, vmax=1, cmap=plt.cm.summer)
plt.title('NDVI')
plt.colorbar(img_ndvi)
ax.gridlines() #draw_labels=True,
ndvi_plot = vs.fig2plot(fig, output_dir='.', file_extension=file_extension, dpi=dpi, figsize=figsize)
return ndvi_plot # ndvi_plot
extent=extent,
origin='upper',
norm=norm,
vmin=-1,
vmax=1,
cmap=plt.cm.BrBG)
# img_ndvi = ax.imshow(data, extent=extent, transform=projection, # [:3, :, :].transpose((1, 2, 0))
# origin='upper',norm=norm, vmin=-1, vmax=1, cmap=plt.cm.summer)
plt.title('NDVI')
plt.colorbar(img_ndvi)
ax.gridlines() #draw_labels=True,
from flyingpigeon import visualisation as vs
ndvi_plot = vs.fig2plot(fig, output_dir='.')
#
#
# from osgeo import gdal, osr
# from matplotlib import pyplot as plt
# from flyingpigeon import visualisation as vs
#
# gdal.UseExceptions()
#
# fname = '/home/nils/data/ndvi_SENTINE2ogmvEh.tif'
#
# ds = gdal.Open(fname)
# # data = ds.ReadAsArray()
# gt = ds.GetGeoTransform()
# proj = ds.GetProjection()
ax.add_patch(
mpatches.Polygon(xy + 2,
closed=True,
transform=ccrs.PlateCarree(),
color='coral',
alpha=0.6))
# ccrs.Geodetic()
ax.gridlines(draw_labels=True, )
# plt.show()
#
# ax.add_patch(polygon)
from flyingpigeon import visualisation as vs
img = vs.fig2plot(fig, output_dir='/home/nils/data')
print img
plt.show()
# xy = np.array([[14.091911258909906, 8.141018593002007],
# [15.088597794983281, 8.142024478978072],
# [15.088393218629665, 7.14880742704817],
# [14.09400773070998, 7.147925563681057],
# [14.091911258909906, 8.141018593002007]])
#
#
# fig, ax = plt.subplots()
# patches = []
# num_polygons = 5
# num_sides = 5
