def get_raster(filename, band_index, minimum=0, maximum=65535):
"""
Get a single band image.
Possible errors:
* File doesn't exist
* Band doesn't exist
"""
fpath = os.path.join(DATA_DIR, filename)
if not os.path.exists(fpath):
return jsonify({'error': 'File does not exist'})
with rasterio.drivers():
with rasterio.open(fpath) as src:
if not band_index in map(lambda x: x + 1, range(7)):
return jsonify({'error': 'Band index out of range'})
band = src.read_band(band_index)
zoom = 600.0 / band.shape[1]
band = ndimage.interpolation.zoom(band, zoom)
output = scale_image(band, float(minimum), float(maximum))
return send_file(output, mimetype='image/png')
def get_color(filepath, minimum, maximum):
"""
Get a color image.
"""
fpath = os.path.join(DATA_DIR, filepath)
if not os.path.exists(fpath):
return jsonify({'error': 'File does not exist'})
with rasterio.drivers():
with rasterio.open(fpath) as src:
def get_band(index):
band = src.read_band(index)
zoom = 600.0 / band.shape[1]
return ndimage.interpolation.zoom(band, zoom)
count = src.meta["count"]
bands = map(get_band, get_color_bands(count))
arr = np.dstack(bands)
minimum, maximum = arr.min(), arr.max()
output = scale_image(arr, float(minimum), float(maximum))
return send_file(output, mimetype='image/png')
def get_raster_test(fpath, band_index, minimum, maximum):
"""
Testing new function that reads a raster from memory.
"""
print "GET RASTER TEST"
if BANDS is None:
return jsonify({'error': 'Image is not in memory'})
if band_index == 0:
if BANDS.shape[2] == 3:
arr = BANDS[:, :, 0:3]
else:
arr = BANDS[:, :, 2::-1]
else:
arr = BANDS[:, :, band_index - 1]
minimum, maximum = arr.min(), arr.max()
output = scale_image(arr, float(minimum), float(maximum))
return send_file(output, mimetype='image/png')
def get_color_composite(filepath, r, g, b):
fpath = os.path.join(DATA_DIR, filepath)
if not os.path.exists(fpath):
return jsonify({'error': 'File does not exist'})
with rasterio.drivers():
with rasterio.open(fpath) as src:
zoom = IMG_WIDTH / src.meta["width"]
def get_band(index):
band = src.read_band(index)
return ndimage.interpolation.zoom(band, zoom)
arr = np.dstack(
map(get_band, [r, g, b])
)
minimum, maximum = arr.min(), arr.max()
output = scale_image(arr, float(minimum), float(maximum))
return send_file(output, mimetype='image/png')
