def test_px2lonlat_gt(self):
lon_array = np.array([self.col])
lat_array = np.array([self.row])
lon, lat = gt.px2lonlat_gt(self.img, lon_array, lat_array,
self.lon_min, self.lat_min, self.lon_max,
self.lat_max)
assert self.lat == lat[0]
assert self.lon == lon[0]
def test_px2lonlat_gt(self):
lon_array = np.array([self.col])
lat_array = np.array([self.row])
lon, lat = gt.px2lonlat_gt(self.img, lon_array, lat_array,
self.lon_min, self.lat_min,
self.lon_max, self.lat_max)
assert self.lat == lat[0]
assert self.lon == lon[0]
def bbox_img(source_file, region, fileExtension, shapefile=None):
"""
Clips bounding box out of image file and returns data as numpy.ndarray
Parameters
----------
source_file : str
Path to source file.
region : str
Identifier of the region in the shapefile. If the default shapefile is
used, this would be the FIPS country code.
fileExtension : str
Filetype (e.g. png, tif).
shapefile : str, optional
Path to shape file, uses "world country admin boundary shapefile" by
default.
Returns
-------
data : dict of numpy.arrays
Clipped image (grey values).
lon_new : numpy.array
Longitudes of the clipped image.
lat_new : numpy.array
Latitudes of the clipped image.
timestamp : datetime.date
Timestamp of the image.
metadata : dict of strings
Metadata from source netCDF file.
"""
orig_img = Image.open(source_file)
lon_min_src, lat_min_src, lon_max_src, lat_max_src = \
get_layer_extent(source_file)
if region == 'global':
lon_min = -180
lon_max = 180
lat_min = -90
lat_max = 90
else:
shp = Shape(region, shapefile)
lon_min = shp.bbox[0]
lon_max = shp.bbox[2]
lat_min = shp.bbox[1]
lat_max = shp.bbox[3]
d = lon_max - lon_min
# countries that cross the international dateline (maybe more!)
if region in ['NZ', 'RS', 'US']:
lon_min, lon_max = dateline_country(region)
# get 2 pairs of points (upper left, lower right of bbox)
if d > 350 and region not in ['AY', 'global']:
if fileExtension in ['.tif', '.tiff', '.TIF', '.TIFF']:
if (round(lon_max_src - lon_min_src) == 360.0
and round(lat_max_src - lat_min_src) == 180.0):
orig_img = rearrange_img(orig_img)
row_min, col_min = lonlat2px_rearr(orig_img, lon_min, lat_max)
row_max, col_max = lonlat2px_rearr(orig_img, lon_max, lat_min)
img = orig_img.crop(
(int(math.floor(col_min)), int(math.floor(row_min)),
int(math.ceil(col_max)), int(math.ceil(row_max))))
else:
print 'Rearranging is only possible for global imagefiles.'
return
else:
orig_img = rearrange_img(orig_img)
row_min, col_min = lonlat2px_rearr(orig_img, lon_min, lat_max)
row_max, col_max = lonlat2px_rearr(orig_img, lon_max, lat_min)
img = orig_img.crop(
(int(math.floor(col_min)), int(math.floor(row_min)),
int(math.ceil(col_max)), int(math.ceil(row_max))))
elif fileExtension in ['.tif', '.tiff', '.TIF', '.TIFF']:
row_min, col_min = lonlat2px_gt(orig_img, lon_min, lat_max,
lon_min_src, lat_min_src, lon_max_src,
lat_max_src)
row_max, col_max = lonlat2px_gt(orig_img, lon_max, lat_min,
lon_min_src, lat_min_src, lon_max_src,
lat_max_src)
# crop image
img = orig_img.crop(
(int(math.floor(col_min)), int(math.floor(row_min)),
int(math.ceil(col_max)), int(math.ceil(row_max))))
else:
row_min, col_min = lonlat2px(orig_img, lon_min, lat_max)
row_max, col_max = lonlat2px(orig_img, lon_max, lat_min)
# crop image
img = orig_img.crop(
(int(math.floor(col_min)), int(math.floor(row_min)),
int(math.ceil(col_max)), int(math.ceil(row_max))))
# get data values from image
data = {'dataset': np.array(img)}
# lon_new, lat_new
lon_px = np.arange(int(math.floor(col_min)), int(math.ceil(col_max)))
lat_px = np.arange(int(math.floor(row_min)), int(math.ceil(row_max)))
if region in ['NZ', 'RS', 'US']:
lon_new, lat_new = px2lonlat_rearr(orig_img, lon_px, lat_px)
elif fileExtension in ['.tif', '.tiff', '.TIF', '.TIFF']:
lon_new, lat_new = px2lonlat_gt(orig_img, lon_px, lat_px, lon_min_src,
lat_min_src, lon_max_src, lat_max_src)
else:
lon_new, lat_new = px2lonlat(orig_img, lon_px, lat_px)
# timestamp
timestamp = None
# metadata
metadata = None
# move coordinates to pixel center
if lon_new.size > 2:
lon_new += (lon_new[1] - lon_new[0]) / 2
if lat_new.size > 2:
lat_new += (lat_new[1] - lat_new[0]) / 2
return data, lon_new, lat_new, timestamp, metadata
def bbox_img(source_file, region, fileExtension, shapefile=None):
"""
Clips bounding box out of image file and returns data as numpy.ndarray
Parameters
----------
source_file : str
Path to source file.
region : str
Identifier of the region in the shapefile. If the default shapefile is
used, this would be the FIPS country code.
fileExtension : str
Filetype (e.g. png, tif).
shapefile : str, optional
Path to shape file, uses "world country admin boundary shapefile" by
default.
Returns
-------
data : dict of numpy.arrays
Clipped image (grey values).
lon_new : numpy.array
Longitudes of the clipped image.
lat_new : numpy.array
Latitudes of the clipped image.
timestamp : datetime.date
Timestamp of the image.
metadata : dict of strings
Metadata from source netCDF file.
"""
orig_img = Image.open(source_file)
lon_min_src, lat_min_src, lon_max_src, lat_max_src = \
get_layer_extent(source_file)
if region == 'global':
lon_min = -180
lon_max = 180
lat_min = -90
lat_max = 90
else:
shp = Shape(region, shapefile)
lon_min = shp.bbox[0]
lon_max = shp.bbox[2]
lat_min = shp.bbox[1]
lat_max = shp.bbox[3]
d = lon_max - lon_min
# countries that cross the international dateline (maybe more!)
if region in ['NZ', 'RS', 'US']:
lon_min, lon_max = dateline_country(region)
# get 2 pairs of points (upper left, lower right of bbox)
if d > 350 and region not in ['AY', 'global']:
if fileExtension in ['.tif', '.tiff', '.TIF', '.TIFF']:
if (round(lon_max_src - lon_min_src) == 360.0 and
round(lat_max_src - lat_min_src) == 180.0):
orig_img = rearrange_img(orig_img)
row_min, col_min = lonlat2px_rearr(orig_img, lon_min, lat_max)
row_max, col_max = lonlat2px_rearr(orig_img, lon_max, lat_min)
img = orig_img.crop((int(math.floor(col_min)),
int(math.floor(row_min)),
int(math.ceil(col_max)),
int(math.ceil(row_max))))
else:
print 'Rearranging is only possible for global imagefiles.'
return
else:
orig_img = rearrange_img(orig_img)
row_min, col_min = lonlat2px_rearr(orig_img, lon_min, lat_max)
row_max, col_max = lonlat2px_rearr(orig_img, lon_max, lat_min)
img = orig_img.crop((int(math.floor(col_min)),
int(math.floor(row_min)),
int(math.ceil(col_max)),
int(math.ceil(row_max))))
elif fileExtension in ['.tif', '.tiff', '.TIF', '.TIFF']:
row_min, col_min = lonlat2px_gt(orig_img, lon_min, lat_max,
lon_min_src, lat_min_src, lon_max_src,
lat_max_src)
row_max, col_max = lonlat2px_gt(orig_img, lon_max, lat_min,
lon_min_src, lat_min_src, lon_max_src,
lat_max_src)
# crop image
img = orig_img.crop((int(math.floor(col_min)),
int(math.floor(row_min)),
int(math.ceil(col_max)),
int(math.ceil(row_max))))
else:
row_min, col_min = lonlat2px(orig_img, lon_min, lat_max)
row_max, col_max = lonlat2px(orig_img, lon_max, lat_min)
# crop image
img = orig_img.crop((int(math.floor(col_min)),
int(math.floor(row_min)),
int(math.ceil(col_max)),
int(math.ceil(row_max))))
# get data values from image
data = {'dataset': np.array(img)}
# lon_new, lat_new
lon_px = np.arange(int(math.floor(col_min)), int(math.ceil(col_max)))
lat_px = np.arange(int(math.floor(row_min)), int(math.ceil(row_max)))
if region in ['NZ', 'RS', 'US']:
lon_new, lat_new = px2lonlat_rearr(orig_img, lon_px, lat_px)
elif fileExtension in ['.tif', '.tiff', '.TIF', '.TIFF']:
lon_new, lat_new = px2lonlat_gt(orig_img, lon_px, lat_px, lon_min_src,
lat_min_src, lon_max_src, lat_max_src)
else:
lon_new, lat_new = px2lonlat(orig_img, lon_px, lat_px)
# timestamp
timestamp = None
# metadata
metadata = None
# move coordinates to pixel center
if lon_new.size > 2:
lon_new += (lon_new[1] - lon_new[0]) / 2
if lat_new.size > 2:
lat_new += (lat_new[1] - lat_new[0]) / 2
return data, lon_new, lat_new, timestamp, metadata
