def tiftile():
x = request.args.get('x')
y = request.args.get('y')
z = request.args.get('z')
tifName = request.args.get('tname')
tifPath1 = "d:\\data\\3010\\{}.tif".format(tifName)
try:
dataset = rasterio.open(tifPath1)
tile, mask = reader.tile(dataset, int(x), int(y), int(z), tilesize=256)
dataset.close()
min = 0
max = 60
renderData = np.array([tile[0], tile[1]+tile[2]*0.3, tile[2]])
renderData = renderData.astype(np.uint8)
mtdata = to_math_type(renderData)
data = sigmoidal(mtdata, 10, 0.15)*255
buffer = render(data.astype(np.uint8), mask=mask)
return send_file(io.BytesIO(buffer), mimetype="image/png", attachment_filename="{}_{}_{}.jpg".format(x, y, z))
except Exception as a:
print(a)
return abort(404)
finally:
pass
def test_sigmoidal(arr):
x = sigmoidal(arr, 10, 0.15)
assert x[0][0][0] - 0.08056034 < 1e-4
# contrast < 0
x = sigmoidal(arr, -10, 0.15)
assert x[0][0][0] - 0.020186627 < 1e-4
# bias zero, make it a tiny epsilon
x = sigmoidal(arr, 10, 0)
assert x[0][0][0] - 0.19362122 < 1e-4
# contrast zzero, arrays are equal
x = sigmoidal(arr, 0, 0.15)
assert np.array_equal(x, arr)
# output contains NaN
with pytest.raises(ValueError):
x = sigmoidal(arr, 100, -0.5)
# output is not within the range of 0..1
with pytest.raises(ValueError):
arr[0][0][0] = 1.0
arr[0][0][1] = 2.0
x = sigmoidal(arr, 10, -0.5)
def execute(mp):
"""Enhance RGB colors from a Sentinel-2 SAFE archive."""
# read input SAFE file
with mp.open(
"input_file", resampling=mp.params["resampling"]
) as safe_file:
try:
# read red, green and blue bands & scale to 8 bit
rgb = np.clip(
safe_file.read(
[4, 3, 2],
mask_clouds=mp.params["mask_clouds"],
mask_white_areas=mp.params["mask_white_areas"]
) / 16, 0, 255
).astype("uint8")
except MapcheteEmptyInputTile:
return "empty"
# scale to 0 to 1 for filters
red, green, blue = rgb.astype("float") / 255
# save nodata mask for later & remove rgb to free memory
mask = rgb.mask
del rgb
# using rio-color:
# (1) apply gamma correction to each band individually
# (2) add sigmoidal contrast & bias
# (3) add saturation
enhanced = np.clip(
saturation(
sigmoidal(
np.stack([
# apply gamma correction for each band
gamma(red, mp.params["red_gamma"]),
gamma(green, mp.params["green_gamma"]),
gamma(blue, mp.params["blue_gamma"]),
]),
mp.params["sigmoidal_contrast"],
mp.params["sigmoidal_bias"]
),
mp.params["saturation"]
) * 255, # scale back to 8bit
1, 255 # clip valid values from 1 to 255
)
# use original nodata mask and return
return np.where(mask, 0, enhanced).astype("uint8")
band = rasterio.band(dataset, 1)
print(band)
tile, mask = reader.tile(dataset, 852, 418, 10, tilesize=1024)
min = 0
max = 60
tile1 = (tile[0]-min)/max*255
tile2 = (tile[1]-min)/max*255
tile3 = (tile[2]-min)/max*255
tileList = np.array([tile[0], tile[1], tile[2]])
renderData = np.where(tileList > 255, 255, tileList)
renderData = np.where(renderData < 0, 0, renderData)
renderData = renderData.astype(np.uint8)
mtdata = to_math_type(renderData)
data = gamma(mtdata, 1.7)
data = sigmoidal(mtdata, 10, 0)*255
buffer = render(data.astype(np.uint8), mask=mask)
with open("reraster2.png", "wb") as f:
f.write(buffer)
def test_parse_comma(arr):
# Commas are optional whitespace, treated like empty string
f1, f2 = parse_operations("gamma r,g,b 0.95, sigmoidal r,g,b 35 0.13")
assert np.array_equal(
f2(f1(arr)), sigmoidal(gamma(arr, g=0.95), contrast=35, bias=0.13))
def test_parse_multi(arr):
f1, f2 = parse_operations("gamma rgb 0.95 sigmoidal rgb 35 0.13")
assert np.array_equal(
f2(f1(arr)), sigmoidal(gamma(arr, g=0.95), contrast=35, bias=0.13))
def test_parse_sigmoidal(arr):
f = parse_operations("sigmoidal rgb 5 0.53")[0]
assert np.array_equal(f(arr), sigmoidal(arr, contrast=5, bias=0.53))