def get_input():
''' Method called for POST `/getInput`
'''
bottle.response.content_type = 'application/json'
data = bottle.request.json
data["remote_address"] = bottle.request.client_ip
SESSION_HANDLER.get_session(bottle.request.session.id).add_entry(
data) # record this interaction
# Inputs
extent = data["extent"]
dataset = data["dataset"]
if dataset not in DATASETS:
raise ValueError(
"Dataset doesn't seem to be valid, please check Datasets.py")
naip_data, naip_crs, naip_transform, naip_bounds, naip_index = DATASETS[
dataset]["data_loader"].get_data_from_extent(extent)
naip_data = np.rollaxis(naip_data, 0, 3)
naip_data, new_bounds = warp_data_to_3857(naip_data, naip_crs,
naip_transform, naip_bounds)
naip_data = crop_data_by_extent(naip_data, new_bounds, extent)
naip_img = naip_data[:, :, :3].copy().astype(
np.uint8) # keep the RGB channels to save as a color image
naip_img = cv2.imencode(".png",
cv2.cvtColor(naip_img,
cv2.COLOR_RGB2BGR))[1].tostring()
naip_img = base64.b64encode(naip_img).decode("utf-8")
data["input_naip"] = naip_img
bottle.response.status = 200
return json.dumps(data)
def pred_tile():
''' Method called for POST `/predTile`'''
bottle.response.content_type = 'application/json'
data = bottle.request.json
data["remote_address"] = bottle.request.client_ip
SESSION_HANDLER.get_session(bottle.request.session.id).add_entry(
data) # record this interaction
# Inputs
geom = data["polygon"]
class_list = data["classes"]
name_list = [item["name"] for item in class_list]
color_list = [item["color"] for item in class_list]
dataset = data["dataset"]
zone_layer_name = data["zoneLayerName"]
if dataset not in DATASETS:
raise ValueError(
"Dataset doesn't seem to be valid, do the datasets in js/tile_layers.js correspond to those in TileLayers.py"
)
try:
naip_data, raster_profile, raster_transform, raster_bounds, raster_crs = DATASETS[
dataset]["data_loader"].get_data_from_shape(geom["geometry"])
naip_data = np.rollaxis(naip_data, 0, 3)
shape_area = get_area_from_geometry(geom["geometry"])
except NotImplementedError as e:
bottle.response.status = 400
return json.dumps({
"error":
"Cannot currently download imagery with 'Basemap' based datasets"
})
output = SESSION_HANDLER.get_session(bottle.request.session.id).model.run(
naip_data, geom, True)
output_hard = output.argmax(axis=2)
print("Finished, output dimensions:", output.shape)
# apply nodata mask from naip_data
nodata_mask = np.sum(naip_data == 0, axis=2) == naip_data.shape[2]
output_hard[nodata_mask] = 255
vals, counts = np.unique(output_hard[~nodata_mask], return_counts=True)
# ------------------------------------------------------
# Step 4
# Convert images to base64 and return
# ------------------------------------------------------
tmp_id = get_random_string(8)
img_hard = np.round(
class_prediction_to_img(output, True, color_list) * 255,
0).astype(np.uint8)
img_hard = cv2.cvtColor(img_hard, cv2.COLOR_RGB2BGRA)
img_hard[nodata_mask] = [0, 0, 0, 0]
img_hard, img_hard_bounds = warp_data_to_3857(img_hard,
raster_crs,
raster_transform,
raster_bounds,
resolution=10)
cv2.imwrite(os.path.join(ROOT_DIR, "downloads/%s.png" % (tmp_id)),
img_hard)
data["downloadPNG"] = "downloads/%s.png" % (tmp_id)
new_profile = raster_profile.copy()
new_profile['driver'] = 'GTiff'
new_profile['dtype'] = 'uint8'
new_profile['compress'] = "lzw"
new_profile['count'] = 1
new_profile['transform'] = raster_transform
new_profile['height'] = naip_data.shape[0]
new_profile['width'] = naip_data.shape[1]
new_profile['nodata'] = 255
f = rasterio.open(os.path.join(ROOT_DIR, "downloads/%s.tif" % (tmp_id)),
'w', **new_profile)
f.write(output_hard.astype(np.uint8), 1)
f.close()
data["downloadTIFF"] = "downloads/%s.tif" % (tmp_id)
f = open(os.path.join(ROOT_DIR, "downloads/%s.txt" % (tmp_id)), "w")
f.write("Class id\tClass name\tPercent area\tArea (km^2)\n")
for i in range(len(vals)):
pct_area = (counts[i] / np.sum(counts))
if shape_area is not None:
real_area = shape_area * pct_area
else:
real_area = -1
f.write("%d\t%s\t%0.4f%%\t%0.4f\n" %
(vals[i], name_list[vals[i]], pct_area * 100, real_area))
f.close()
data["downloadStatistics"] = "downloads/%s.txt" % (tmp_id)
bottle.response.status = 200
return json.dumps(data)
def pred_patch():
''' Method called for POST `/predPatch`'''
bottle.response.content_type = 'application/json'
data = bottle.request.json
data["remote_address"] = bottle.request.client_ip
SESSION_HANDLER.get_session(bottle.request.session.id).add_entry(
data) # record this interaction
# Inputs
extent = data["extent"]
dataset = data["dataset"]
class_list = data["classes"]
name_list = [item["name"] for item in class_list]
color_list = [item["color"] for item in class_list]
# ------------------------------------------------------
# Step 1
# Transform the input extent into a shapely geometry
# Find the tile assosciated with the geometry
# ------------------------------------------------------
# ------------------------------------------------------
# Step 2
# Load the input data sources for the given tile
# ------------------------------------------------------
if dataset not in DATASETS:
raise ValueError(
"Dataset doesn't seem to be valid, do the datasets in js/tile_layers.js correspond to those in TileLayers.py"
)
naip_data, naip_crs, naip_transform, naip_bounds, naip_index = DATASETS[
dataset]["data_loader"].get_data_from_extent(extent)
naip_data = np.rollaxis(
naip_data, 0, 3
) # we do this here instead of get_data_by_extent because not all GeoDataTypes will have a channel dimension
SESSION_HANDLER.get_session(
bottle.request.session.id).current_transform = (naip_crs,
naip_transform,
naip_index)
# ------------------------------------------------------
# Step 3
# Run a model on the input data
# Apply reweighting
# Fix padding
# ------------------------------------------------------
output = SESSION_HANDLER.get_session(bottle.request.session.id).model.run(
naip_data, extent, False)
assert len(
output.shape
) == 3, "The model function should return an image shaped as (height, width, num_classes)"
assert (
output.shape[2] < output.shape[0] and output.shape[2] < output.shape[1]
), "The model function should return an image shaped as (height, width, num_classes)" # assume that num channels is less than img dimensions
# ------------------------------------------------------
# Step 4
# Warp output to EPSG:3857 and crop off the padded area
# ------------------------------------------------------
output, output_bounds = warp_data_to_3857(output, naip_crs, naip_transform,
naip_bounds)
output = crop_data_by_extent(output, output_bounds, extent)
# ------------------------------------------------------
# Step 5
# Convert images to base64 and return
# ------------------------------------------------------
img_soft = np.round(
class_prediction_to_img(output, False, color_list) * 255,
0).astype(np.uint8)
img_soft = cv2.imencode(".png",
cv2.cvtColor(img_soft,
cv2.COLOR_RGB2BGR))[1].tostring()
img_soft = base64.b64encode(img_soft).decode("utf-8")
data["output_soft"] = img_soft
img_hard = np.round(
class_prediction_to_img(output, True, color_list) * 255,
0).astype(np.uint8)
img_hard = cv2.imencode(".png",
cv2.cvtColor(img_hard,
cv2.COLOR_RGB2BGR))[1].tostring()
img_hard = base64.b64encode(img_hard).decode("utf-8")
data["output_hard"] = img_hard
bottle.response.status = 200
return json.dumps(data)