bgimg.convert("RGB")
fls_img = Image(fls_img.channels[0], mode='L')
fls_img.colorize(fogcol)
fls_img.merge(bgimg)
ele_img = Image(ele_img.channels[0], mode='L')
#cwp_img = Image(cwp_img.channels[0], mode='L')
#cwp_masked = np.ma.array(cwp_ger.image_data, mask=fogmask)
#print(np.histogram(cwp_masked.compressed()))
#ele_img.show()
#cwp_img.show()
#overview.show()
#fls_img.show()
#snow_rgb.show()
#daymicro_rgb.show()
ele_img.save("/tmp/fog_example_msg_ger_elevation_{}.png".format(
time.strftime("%Y%m%d%H%M")))
cot_img.save("/tmp/fog_example_msg_ger_cot_{}.png".format(
time.strftime("%Y%m%d%H%M")))
reff_img.save("/tmp/fog_example_msg_ger_reff_{}.png".format(
time.strftime("%Y%m%d%H%M")))
cwp_img.save("/tmp/fog_example_msg_ger_cwp_{}.png".format(
time.strftime("%Y%m%d%H%M")))
fls_img.save("/tmp/fog_example_msg_ger_fls_{}.png".format(
time.strftime("%Y%m%d%H%M")))
overview.save("/tmp/fog_example_msg_ger_overview_{}.png".format(
time.strftime("%Y%m%d%H%M")))
snow_rgb.save("/tmp/fog_example_msg_ger_snow_rgb_{}.png".format(
time.strftime("%Y%m%d%H%M")))
daymicro_rgb.save("/tmp/fog_example_msg_ger_daymicro_rgb_{}.png".format(
time.strftime("%Y%m%d%H%M")))
#germ_scene["HRV"].show()
def create_PIL_image(rgb, data, in_msg):
if in_msg.verbose:
print("*** make image for: ", rgb)
# get the data array that you want to plot
if rgb in products.MSG:
prop = data[rgb].data
plot_type = 'channel_image'
elif rgb in products.MSG_color:
prop = data[rgb.replace("c", "")].data
plot_type = 'trollimage'
elif rgb in products.CTTH:
prop = data[rgb].data
prop.mask = (prop == 0)
if rgb == 'CTH':
prop /= 1000. # 1000. == m -> km
plot_type = 'trollimage'
elif rgb in products.CT:
prop = data[rgb].data
plot_type = 'palette'
if rgb == 'CT_QUALITY':
plot_type = 'trollimage'
elif rgb in products.CMa or rgb in products.SPhR:
prop = data[rgb].data
if hasattr(data[rgb], 'palette') and in_msg.nwcsaf_calibrate == False:
plot_type = 'palette'
else:
plot_type = 'trollimage'
elif rgb in products.HSAF:
prop = data[rgb].data
plot_type = 'trollimage'
else:
# includes products.RGBs_buildin
prop = ma.asarray([-999., -999.])
plot_type = 'user_defined'
#from numpy import log10
#prop=log10(prop)
# search minimum and maximum
# (default)
min_data = prop.min()
max_data = prop.max()
# replace default with fixed min/max if specified
if in_msg.fixed_minmax:
if rgb in list(in_msg.rad_min.keys()):
min_data = in_msg.rad_min[rgb]
else:
if rgb not in products.RGBs_buildin:
print(
"*** Warning, no specified minimum for plotting in get_input_msg.py or input file"
)
if rgb in list(in_msg.rad_max.keys()):
max_data = in_msg.rad_max[rgb]
else:
if rgb not in products.RGBs_buildin:
print(
"*** Warning, no specified maximum for plotting in get_input_msg.py or input file"
)
if in_msg.verbose and rgb not in products.RGBs_buildin:
print('... set value range from min_data (', min_data,
') to max_data (', max_data, ')')
# specifies if a colorbar does make sense at all
in_msg.colormap = {}
# make the image
if plot_type == 'channel_image':
if in_msg.verbose:
print(
" use data.image.channel_image for black and white pictures"
)
img = data.image.channel_image(rgb)
in_msg.colormap[rgb] = None
elif plot_type == 'trollimage':
if in_msg.verbose:
print(
" use trollimage.image.image for colorized pictures (min=" +
str(min_data) + ", max=" + str(max_data) + ")")
img = trollimage(prop, mode="L", fill_value=in_msg.fill_value)
rainbow.set_range(min_data, max_data)
img.colorize(rainbow)
rainbow_r.set_range(
min_data, max_data
) # attention set_range does modify the colormap, but does not have a return values !
in_msg.colormap[rgb] = rainbow.reverse()
# print "in_msg.colormap[rgb]", rgb, in_msg.colormap[rgb]
elif plot_type == 'palette':
min_data = 0.
max_data = float(len(data[rgb].palette) - 1)
if in_msg.verbose:
print(" use GeoImage and colorize with a palette (min=" +
str(min_data) + ", max=" + str(max_data) + ")")
img = GeoImage(prop,
data.area,
data.time_slot,
mode="P",
palette=data[rgb].palette,
fill_value=in_msg.fill_value)
colormap = convert_palette2colormap(data[rgb].palette)
colormap.set_range(min_data, max_data) # no return value!
in_msg.colormap[rgb] = colormap
elif plot_type == 'user_defined':
obj_image = get_image(data, rgb)
if in_msg.verbose:
print(" use image function defined by my_msg_module.py")
img = obj_image()
in_msg.colormap[rgb] = None
#if rgb == 'ndvi':
# in_msg.colormap[rgb] = rdylgn_r
else:
print("*** Error in create_PIL_image (" +
inspect.getfile(inspect.currentframe()) + ")")
print(" unknown plot_type ", plot_type)
quit()
if in_msg.HRV_enhancement:
if in_msg.verbose:
print("enhance the image with the HRV channel")
luminance = GeoImage((data["HRV"].data),
data.area,
data.time_slot,
crange=(0, 100),
mode="L")
luminance.enhance(gamma=2.0)
img.replace_luminance(luminance.channels[0])
rgb = 'HR' + rgb
## alternative: for geoimages is possible to add coasts and borders, but not for trollimage
#if hasattr(img, 'add_overlay'):
# if in_msg.verbose:
# print " add coastlines to image by add_averlay"
# img.add_overlay(color=(0, 0, 0), width=0.5, resolution=None)
# convert image to PIL image
if hasattr(img, 'pil_image'):
if in_msg.verbose:
print(" convert to PIL_image by pil_image function")
PIL_image = img.pil_image()
else:
if in_msg.verbose:
print(" convert to PIL_image by saving and reading")
tmp_file = outputDir + satS + '_' + dateS + '_' + timeS + '__' + area + '_' + rgb.replace(
"_", "-") + '_tmp.png' # in_msg.
img.save(tmp_file)
PIL_image = Image.open(tmp_file)
subprocess.call("rm " + tmp_file, shell=True)
return PIL_image
