def load_objs(objects):
if isinstance(objects, PurePath):
objects = str(objects)
# Load objects
# TODO: Read in chunks, parallelize, recombine and write
logger.info('Reading in objects...')
# objs = gpd.read_file(objects)
objs = read_vec(objects)
logger.info('Objects found: {:,}'.format(len(objs)))
return objs
def __init__(self, objects_path, value_fields=None):
if isinstance(objects_path, gpd.GeoDataFrame):
self.objects = copy.deepcopy(objects_path)
self.objects_path = None
else:
self.objects_path = objects_path
self.objects = read_vec(objects_path)
logger.info('Loaded {:,} objects.'.format(len(self.objects)))
# Field names
self.nebs_fld = 'neighbors'
self._area_fld = 'area'
self.pseudo_area_fld = 'pseudo_area'
self.compact_fld = 'compactness'
self.class_fld = 'class'
# Merge column names
self.mc_fld = 'merge_candidates'
self.mp_fld = 'merge_path'
# Inits to True, marked False if merged, or considered and unmergeable
self.m_fld = 'mergeable'
self.m_seed_fld = 'merge_seed'
self.m_ct_fld = 'merge_count'
self.continue_iter = 'continue_iter'
self.mergeable_ids = None
# List of (field_name, summary_stat) to be recalculated after merging
self.value_fields = self._parse_value_fields(value_fields)
# List of boolean fields holding result of apply a rule
self.rule_fields = []
# Properties calculated on demand
self._num_objs = None
self._fields = list(self.objects.columns)
self._object_stats = None
self._area = None
# Neighbor value fields
self.nv_fields = list()
self.objects[self.nebs_fld] = np.NaN
# Rules
self._rule_fld_name = 'in_field' # field name in rule dictionaries
# TODO: check for unique index, create if not
# Name index if unnamed
if not self.objects.index.name:
self.objects.index.name = 'index'
if not self.objects.index.is_unique:
logger.warning('Non-unique index not supported.')
def get_footprint_dems(footprint_path, filepath=get_filepath_field(),
dem_name='DEM_NAME', dem_path_fld='dem_path',
dem_exist_fld='dem_exist', location=None,
use_terrranova=False):
"""Load Footprint - Check for existence of DEMs"""
logger.debug('Footprint type: {}'.format(type(footprint_path)))
if isinstance(footprint_path, list):
# List of paths
fp = pd.DataFrame({dem_path_fld: footprint_path})
else:
if isinstance(footprint_path, str):
if os.path.splitext(footprint_path) == '.txt':
# Paths in text file
with open(footprint_path, 'r') as src:
content = src.readlines()
fp = pd.DataFrame({dem_path_fld: content})
else:
# Load footprint
logger.info('Loading DEM footprint...')
# fp = gpd.read_file(footprint_path)
fp = read_vec(footprint_path)
# Vector file of footprints with path field
elif isinstance(footprint_path, gpd.GeoDataFrame):
fp = footprint_path
if location is None:
fp[dem_path_fld] = fp.apply(lambda x: get_dem_path(x[filepath], x[dem_name]), axis=1)
else:
fp[dem_path_fld] = fp[location]
if platform.system() == 'Windows':
fp[dem_path_fld] = fp[dem_path_fld].apply(lambda x: mnt2v(x))
if use_terranova:
fp[dem_path_fld] = fp[dem_path_fld].apply(lambda x: x.replace(r"pgc\data\elev",
r"pgc\terrnva_data\elev"))
num_fps = len(fp)
logger.info('Records found: {:,}'.format(num_fps))
logger.info('Verifying DEMs existence at location indicated...')
fp[dem_exist_fld] = fp.apply(lambda x: os.path.exists(x[dem_path_fld]), axis=1)
dem_paths = list(fp[fp[dem_exist_fld] == True][dem_path_fld])
num_exist_fps = len(fp)
if num_exist_fps != num_fps:
logger.warning('Filepaths could not be found for {} records, skipping...'.format(num_fps - num_exist_fps))
return dem_paths
def mask_objs(objs, mask_on, out_mask_img=None, out_mask_vec=None):
if out_mask_img is None:
out_mask_img = r'/vsimem/temp_mask.tif'
if out_mask_vec is None:
out_mask_vec = r'/vsimem/temp_mask.shp'
# Write mask vector (and raster if desired)
logger.info('Creating mask from raster: {}'.format(mask_on))
Raster(mask_on).WriteMaskVector(out_vec=out_mask_vec,
out_mask_img=out_mask_img)
# mask = gpd.read_file(out_mask_vec)
mask = read_vec(out_mask_vec)
not_mask = mask[mask.iloc[:, 0] != '1']
# Select only objects in valid areas of mask
logger.info('Removing objects in masked areas...')
# TODO: use centroids for faster cleanup - Begin
# objs_centroids = copy.deepcopy(objs).set_geometry(objs.geometry.centroid)
# use centroids for faster cleanup - End
keep_objs = gpd.overlay(objs, not_mask)
logger.info('Objects kept: {:,}'.format(len(keep_objs)))
return keep_objs
def calc_zonal_stats(shp,
rasters,
names=None,
stats=['min', 'max', 'mean', 'count', 'median'],
area=True,
compactness=False,
roundness=False,
out_path=None):
"""
Calculate zonal statistics on the given vector file
for each raster provided.
Parameters
----------
shp : os.path.abspath
Vector file to compute zonal statistics for the features in.
out_path : os.path.abspath
Path to write vector file with computed stats. Default is to
add '_stats' suffix before file extension.
rasters : list or os.path.abspath
List of rasters to compute zonal statistics for.
Or path to .txt file of raster paths (one per line)
or path to .json file of
name: {path: /path/to/raster.tif, stats: ['mean']}.
names : list
List of names to use as prefixes for created stats. Order
is order of rasters.
stats : list, optional
List of statistics to calculate. The default is None.
area : bool
True to also compute area of each feature in units of
projection.
compactness : bool
True to also compute compactness of each object
roundness : bool
True to also compute roundess of each object
Returns
-------
out_path.
"""
# Load data
if isinstance(shp, gpd.GeoDataFrame):
seg = shp
else:
logger.info('Reading in segments from: {}...'.format(shp))
seg = read_vec(shp)
logger.info('Segments found: {:,}'.format(len(seg)))
# Determine rasters input type
# TODO: Fix logic here, what if a bad path is passed?
if len(rasters) == 1:
if os.path.exists(rasters[0]):
logger.info('Reading raster file...')
ext = os.path.splitext(rasters[0])[1]
if ext == '.txt':
# Assume text file of raster paths, read into list
logger.info('Reading rasters from text file: '
'{}'.format(rasters[0]))
with open(rasters[0], 'r') as src:
content = src.readlines()
rasters = [c.strip() for c in content]
rasters, names = zip(*(r.split("~") for r in rasters))
logger.info('Located rasters:'.format('\n'.join(rasters)))
for r, n in zip(rasters, names):
logger.info('{}: {}'.format(n, r))
# Create list of lists of stats passed, one for each raster
stats = [stats for i in range(len(rasters))]
elif ext == '.json':
logger.info('Reading rasters from json file:'
' {}'.format(rasters[0]))
rasters, names, stats, bands = load_stats_dict(rasters[0])
else:
# Raster paths directly passed
stats = [stats for i in range(len(rasters))]
elif isinstance(rasters, dict):
rasters, names, stats, bands = load_stats_dict(rasters)
# Confirm all rasters exist before starting
for r in rasters:
if not os.path.exists(r):
logger.error('Raster does not exist: {}'.format(r))
logger.error('FileNotFoundError')
# Iterate rasters and compute stats for each
for r, n, s, bs in zip(rasters, names, stats, bands):
if bs is None:
# Split custom stat functions from built-in options
accepted_stats = [
'min', 'max', 'median', 'sum', 'std', 'mean', 'unique',
'range', 'majority'
]
stats_acc = [
k for k in s
if k in accepted_stats or k.startswith('percentile_')
]
# Assume any key not in accepted_stats is a name:custom_fxn
custom_stats = [k for k in stats if k not in accepted_stats]
custom_stats_dict = {}
# for cs in custom_stats:
# custom_stats[cs] = custom_stat_fxn(cs)
seg = compute_stats(gdf=seg, raster=r, name=n, stats=stats_acc)
else:
# Compute stats for each band
for b in bs:
stats_dict = {x: '{}b{}_{}'.format(n, b, x) for x in s}
seg = compute_stats(gdf=seg,
raster=r,
stats=stats_dict,
renamer=stats_dict,
band=b)
# Area recording
if area:
seg['area_zs'] = seg.geometry.area
# Compactness: Polsby-Popper Score -- 1 = circle
if compactness:
seg = apply_compactness(seg)
if roundness:
seg = apply_roundness(seg)
# Write segments with stats to new shapefile
if not out_path:
out_path = os.path.join(
os.path.dirname(shp),
'{}_stats.shp'.format(os.path.basename(shp).split('.')[0]))
if not os.path.exists(os.path.dirname(out_path)):
os.makedirs(os.path.dirname(out_path))
logger.info('Writing segments with statistics to: {}'.format(out_path))
# driver = auto_detect_ogr_driver(out_path, name_only=True)
# seg.to_file(out_path, driver=driver)
write_gdf(seg, out_path)
return out_path
def classify_rts(sub_objects_path,
super_objects_path,
headwall_candidates_out=None,
headwall_candidates_centroid_out=None,
rts_predis_out=None,
rts_candidates_out=None,
aoi_path=None,
headwall_candidates_in=None,
aoi=None):
logger.info('Classifying RTS...')
#%% RULESET
# Headwall Rules
logger.info('Setting up headwall candidate rules...')
# Ruggedness
r_ruggedness = create_rule(rule_type=threshold_rule,
in_field=rug_mean,
op=operator.gt,
threshold=0.2,
out_field=True)
# Surface Area Ratio
r_saratio = create_rule(rule_type=threshold_rule,
in_field=sa_rat_mean,
op=operator.gt,
threshold=1.01,
out_field=True)
# Slope (min)
r_slope_min = create_rule(rule_type=threshold_rule,
in_field=slope_mean,
op=operator.gt,
threshold=8,
out_field=True)
# Slope (max)
r_slope_max = create_rule(rule_type=threshold_rule,
in_field=slope_mean,
op=operator.lt,
threshold=25,
out_field=True)
# NDVI
r_ndvi = create_rule(rule_type=threshold_rule,
in_field=ndvi_mean,
op=operator.lt,
threshold=0,
out_field=True)
# MED
r_med = create_rule(rule_type=threshold_rule,
in_field=med_mean,
op=operator.lt,
threshold=0,
out_field=True)
# Curvature (high)
r_curve = create_rule(rule_type=threshold_rule,
in_field=cur_mean,
op=operator.gt,
threshold=2.5,
out_field=True)
# Difference in DEMs
r_delev = create_rule(rule_type=threshold_rule,
in_field=delev_mean,
op=operator.lt,
threshold=-0.5,
out_field=True)
# All simple threshold rules
r_simple_thresholds = [
r_ruggedness, r_saratio, r_slope_min, r_slope_max, r_ndvi, r_med,
r_curve, r_delev
]
# Adjacency rules
# Adjacent Curvature
r_adj_high_curv = create_rule(rule_type=adj_or_is_rule,
in_field=cur_mean,
op=operator.gt,
threshold=30,
out_field=True)
r_adj_low_curv = create_rule(
rule_type=adj_or_is_rule,
in_field=cur_mean,
op=operator.lt,
threshold=-15, # -30
out_field=True)
# Adjacent MED
r_adj_low_med = create_rule(rule_type=adj_or_is_rule,
in_field=med_mean,
op=operator.lt,
threshold=-0.2,
out_field=True)
# Adjacent or is high edge
# r_adh_high_edge = create_rule(rule_type=adj_or_is_rule,
# in_field=edge_mean,
# op=operator.gt,
# threshold=0.18,
# out_field=True)
# All adjacent rules
r_adj_rules = [r_adj_low_curv, r_adj_high_curv, r_adj_low_med]
#%% RTS Rules
logger.info('Setting up RTS candidate rules...')
r_rts_ndvi = create_rule(rule_type=threshold_rule,
in_field=ndvi_mean,
op=operator.lt,
threshold=0,
out_field=True)
r_rts_med = create_rule(rule_type=threshold_rule,
in_field=med_mean,
op=operator.lt,
threshold=0.1,
out_field=True)
r_rts_slope_low = create_rule(rule_type=threshold_rule,
in_field=slope_mean,
op=operator.gt,
threshold=3,
out_field=True)
r_rts_slope_high = create_rule(rule_type=threshold_rule,
in_field=slope_mean,
op=operator.lt,
threshold=20,
out_field=True)
r_rts_delev = create_rule(rule_type=threshold_rule,
in_field=delev_mean,
op=operator.lt,
threshold=-0.5,
out_field=True)
r_rts_conhw = create_rule(rule_type=threshold_rule,
in_field=contains_hw,
op=operator.eq,
threshold=True,
out_field=True)
r_rts_simple_thresholds = [
r_rts_ndvi, r_rts_med, r_rts_slope_low, r_rts_slope_high, r_rts_delev,
r_rts_conhw
]
#%% HEADWALL CANDIDATES
logger.info('Classifying headwall candidate objects...')
#%% Load candidate headwall objects
if not headwall_candidates_in:
logger.info('Loading headwall candidate objects...')
if aoi_path:
# aoi = gpd.read_file(aoi_path)
aoi = read_vec(aoi_path)
logger.info('Subsetting objects to AOI...')
gdf = select_in_aoi(read_vec(sub_objects_path), aoi, centroid=True)
hwc = ImageObjects(objects_path=gdf, value_fields=value_fields)
else:
hwc = ImageObjects(objects_path=sub_objects_path,
value_fields=value_fields)
#%% Classify headwalls
logger.info('Determining headwall candidates...')
hwc.classify_objects(hw_candidate,
threshold_rules=r_simple_thresholds,
adj_rules=r_adj_rules)
logger.info('Headwall candidates found: {:,}'.format(
len(hwc.objects[hwc.objects[hwc.class_fld] == hw_candidate])))
#%% Write headwall candidates
logger.info('Writing headwall candidates...')
hwc.write_objects(headwall_candidates_out,
to_str_cols=to_str_cols,
overwrite=True)
# if headwall_candidates_centroid_out:
# hwc_centroid = ImageObjects(
# copy.deepcopy(
# hwc.objects.set_geometry(hwc.objects.geometry.centroid)))
# hwc_centroid.write_objects(headwall_candidates_centroid_out,
# overwrite=True)
else:
hwc = ImageObjects(objects_path=headwall_candidates_in,
value_fields=value_fields)
#%% RETROGRESSIVE THAW SLUMPS
#%% Load super objects
logger.info('Loading RTS candidate objects...')
so = ImageObjects(super_objects_path, value_fields=value_fields)
logger.info('Determining RTS candidates...')
#%% Find objects that contain headwalls of a higher elevation than
# themselves
so.objects[contains_hw_gtr] = so.objects.apply(
lambda x: overlay_any_objects(x.geometry,
hwc.objects[hwc.objects[hwc.class_fld] ==
hw_candidate],
predicate='contains',
threshold=x[elev_mean],
other_value_field=elev_mean,
op=operator.gt),
axis=1)
so.objects[contains_hw] = so.objects.apply(lambda x: overlay_any_objects(
x.geometry,
hwc.objects[hwc.objects[hwc.class_fld] == hw_candidate],
predicate='contains',
),
axis=1)
so.objects[contains_hw_cent] = so.objects.apply(
lambda x: overlay_any_objects(x.geometry,
hwc.objects[hwc.objects[hwc.class_fld] ==
hw_candidate],
predicate='contains',
others_centroid=True),
axis=1)
so.objects[contains_hw_gtr] = so.objects.apply(
lambda x: overlay_any_objects(x.geometry,
hwc.objects[hwc.objects[hwc.class_fld] ==
hw_candidate],
predicate='contains',
threshold=x[elev_mean],
other_value_field=elev_mean,
op=operator.gt,
others_centroid=True),
axis=1)
#%% Classify
so.classify_objects(class_name=rts_candidate,
threshold_rules=r_rts_simple_thresholds)
# # Add bool field for RTS candidate or not
# so.objects[rts_cand_bool] = np.where(so.objects[so.class_fld] == rts_candidate,
# 1,
# 0)
logger.info('RTS candidates found: {}'.format(
len(so.objects[so.objects[so.class_fld] == rts_candidate])))
if rts_predis_out:
# Write classified objects before growing
so.write_objects(rts_predis_out,
to_str_cols=to_str_cols,
overwrite=True)
#%% Dissolve touching candidates
rts_dissolved = dissolve_touching(
so.objects[so.objects[so.class_fld] == rts_candidate])
so.objects = pd.concat(
[so.objects[so.objects[so.class_fld] != rts_candidate], rts_dissolved])
#%% Write RTS candidates
logger.info('Writing RTS candidates...')
so.write_objects(rts_candidates_out,
to_str_cols=to_str_cols,
overwrite=True)
return rts_candidates_out
def main(
dem,
dem_prev,
project_dir,
config,
aoi=None,
image=None,
pansh_img=None,
skip_steps=None,
):
# Convert to path objects
if image is not None:
image = Path(image)
dem = Path(dem)
dem_prev = Path(dem_prev)
# %% Get configuration settings
config = get_config(config_file=config)
# Project config settings
project_config = config['project']
EPSG = project_config['EPSG']
if aoi is None:
aoi = Path(project_config['AOI'])
else:
aoi = Path(aoi)
fill_nodata = project_config['fill_nodata']
# Headwall and RTS config settings
hw_config = config['headwall']
rts_config = config['rts']
grow_config = config['grow']
# Preprocessing
pansh_config = config['pansharpen']
BITDEPTH = pansh_config['t']
STRETCH = pansh_config['c']
dem_deriv_config = config[DEM_DERIV]
med_config = dem_deriv_config[MED]
curv_config = dem_deriv_config[CURV]
img_deriv_config = config[IMG_DERIV]
edge_config = img_deriv_config[EDGE_EX]
# %% Build project directory structure
logger.info('Creating project directory structure...')
project_dir = Path(project_dir)
if not project_dir.exists():
logger.info('Creating project parent directory: '
'{}'.format(project_dir))
os.makedirs(project_dir)
SCRATCH_DIR = project_dir / 'scratch'
IMG_DIR = project_dir / 'img'
PANSH_DIR = project_dir / 'pansh'
NDVI_DIR = project_dir / 'ndvi'
DEM_DIR = project_dir / 'dem'
DEM_PREV_DIR = project_dir / dem_prev_k
DEM_DERIV_DIR = DEM_DIR / 'deriv'
SEG_DIR = project_dir / 'seg'
# HW_DIR = SEG_DIR / 'headwall'
HW_SEG_GPKG = SEG_DIR / 'headwall.gpkg'
# RTS_DIR = SEG_DIR / 'rts'
RTS_SEG_GPKG = SEG_DIR / 'rts.gpkg'
# GROW_DIR = SEG_DIR / 'grow'
GROW_SEG_GPKG = SEG_DIR / 'grow.gpkg'
# CLASS_DIR = project_dir / 'classified'
CLASS_GPKG = project_dir / 'classified.gpkg'
for d in [
SCRATCH_DIR,
IMG_DIR,
PANSH_DIR,
NDVI_DIR,
DEM_DIR,
DEM_PREV_DIR,
DEM_DERIV_DIR,
SEG_DIR,
# HW_DIR, RTS_DIR, GROW_DIR, CLASS_DIR
]:
if not d.exists():
os.makedirs(d)
out_vec_fmt = project_config[out_vec_fmt_k]
# %% Imagery Preprocessing
logger.info('\n\n***PREPROCESSING***')
# Pansharpen
if pansh_img is None:
if pan not in skip_steps:
logger.info('Pansharpening: {}'.format(image.name))
pansh_cmd = '{} {} {} -p {} -d {} -t {} -c {} ' \
'--skip-dem-overlap-check'.format(PANSH_PY,
image,
PANSH_DIR,
EPSG,
dem,
BITDEPTH,
STRETCH)
run_subprocess(pansh_cmd)
# Determine output name
pansh_img = PANSH_DIR / '{}_{}{}{}_pansh.tif'.format(
image.stem, bitdepth_lut[BITDEPTH], STRETCH, EPSG)
else:
pansh_img = Path(pansh_img)
# NDVI
if ndvi not in skip_steps:
logger.info('Creating NDVI from: {}'.format(pansh_img.name))
ndvi_cmd = '{} {}'.format(NDVI_PY, pansh_img, NDVI_DIR)
run_subprocess(ndvi_cmd)
# Determine NDVI name
ndvi_img = NDVI_DIR / '{}_ndvi.tif'.format(pansh_img.stem)
# ndvi_img = NDVI_DIR / '{}_ndvi.tif'.format(image.stem)
# %% Clip to AOI
# Organize inputs
inputs = {
img_k: pansh_img,
ndvi_k: ndvi_img,
dem_k: dem,
dem_prev_k: dem_prev,
}
if aoi:
logger.info('Clipping inputs to AOI: {}'.format(aoi))
for k, r in tqdm(inputs.items()):
# out_path = r.parent / '{}{}{}'.format(r.stem, clip_sfx,
# r.suffix)
out_path = project_dir / k / '{}{}{}'.format(
r.stem, clip_sfx, r.suffix)
if clip_step not in skip_steps:
logger.debug('Clipping input {} to AOI: {}'.format(
k, aoi.name))
clip_rasters(str(aoi),
str(r),
out_path=str(out_path),
out_suffix='',
skip_srs_check=True)
inputs[k] = out_path
if fill_nodata:
logger.info('Filling internal NoData gaps in sources...')
for k, r in tqdm(inputs.items()):
# Only fill image no data
if k in [img_k, ndvi_k]:
filled = inputs[k].parent / '{}_filled{}'.format(
inputs[k].stem, inputs[k].suffix)
if fill_step not in skip_steps:
fill_internal_nodata(inputs[k], filled, str(aoi))
inputs[k] = filled
# %% EdgeExtraction
edge_config[img_k] = inputs[img_k]
edge_config[out_dir] = IMG_DIR
edge = otb_ee.create_outname(**edge_config)
if edge_extraction not in skip_steps:
logger.info('Creating EdgeExtraction')
otb_ee.otb_edge_extraction(**edge_config)
inputs[edge_k] = edge
# %% DEM Derivatives
if dem_deriv not in skip_steps:
# DEM Diff
logger.info('Creating DEM Difference...')
logger.info('DEM1: {}'.format(inputs[dem_k]))
logger.info('DEM2: {}'.format(inputs[dem_prev_k]))
diff = DEM_DERIV_DIR / 'dem_diff.tif'
difference_dems(str(inputs[dem_k]),
str(inputs[dem_prev_k]),
out_dem=str(diff))
# Slope
logger.info('Creating slope...')
slope = DEM_DERIV_DIR / '{}_slope{}'.format(dem.stem, dem.suffix)
gdal_dem_derivative(str(inputs[dem_k]), str(slope), 'slope')
# Ruggedness
logger.info('Creating ruggedness index...')
ruggedness = DEM_DERIV_DIR / '{}_rugged{}'.format(dem.stem, dem.suffix)
gdal_dem_derivative(str(inputs[dem_k]), str(ruggedness), 'TRI')
# MED
logger.info('Creating Maximum Elevation Deviation...')
med = wbt_med(str(inputs[dem_k]),
out_dir=str(DEM_DERIV_DIR),
**med_config)
# Curvature
logger.info('Creating profile curvature...')
curvature = wbt_curvature(str(inputs[dem_k]),
out_dir=str(DEM_DERIV_DIR),
**curv_config)
# Surface Area Ratio
logger.info('Creating Surface Area Ratio...')
sar = wbt_sar(str(inputs[dem_k]), out_dir=str(DEM_DERIV_DIR))
inputs[med_k] = med
inputs[curv_k] = curvature
inputs[slope_k] = slope
inputs[rugged_k] = ruggedness
inputs[diff_k] = diff
inputs[sar_k] = sar
# %% SEGMENTATION PREPROCESSING - Segment, calculate zonal statistics
# %%
# HEADWALL
logger.info('\n\n***HEADWALL***')
# Segmentation
hw_config[seg][params][img_k] = inputs[img_k]
# hw_config[seg][params][out_dir] = HW_DIR
hw_seg_out = hw_config[seg][params][out_seg] = str(HW_SEG_GPKG /
'headwall_seg')
if hw_config[seg][alg] == grm:
logger.info('Segmenting subobjects (headwalls)...')
if hw_seg not in skip_steps:
hw_objects = otb_grm.otb_grm(drop_smaller=0.5,
**hw_config[seg][params])
else:
hw_objects = otb_grm.create_outname(**hw_config[seg][params],
name_only=True)
logger.debug('Using provided headwall segmentation: '
'{}'.format(
Path(hw_config[seg][params][out_seg]).relative_to(
project_dir)))
# %% Cleanup
# Create path to write cleaned objects to
# hw_objects = Path(hw_objects)
# cleaned_objects_out = str(hw_objects.parent / '{}{}{}'.format(
# hw_objects.stem, clean_sfx, hw_objects.suffix))
cleaned_objects_out = hw_seg_out + '_cleaned'
if hw_clean not in skip_steps:
if hw_config[cleanup][cleanup]:
logger.info('Cleaning up subobjects...')
cleanup_params = hw_config[cleanup][params]
cleanup_params[mask_on] = str(inputs[dem_k])
# hw_objects = Path(hw_objects)
hw_objects = cleanup_objects(input_objects=hw_seg_out,
out_objects=cleaned_objects_out,
**cleanup_params)
else:
logger.debug('Using provided cleaned headwall objects'
'{}: '.format(
Path(cleaned_objects_out).relative_to(project_dir)))
# hw_objects = cleaned_objects_out
# %% Zonal Stats
logger.info('Calculating zonal statistics on headwall objects...')
# hw_objects_path = Path(hw_objects)
# hw_zs_out_path = '{}_zs{}'.format(
# hw_objects_path.parent / hw_objects_path.stem, hw_objects_path.suffix)
hw_zs_out = cleaned_objects_out + '_zs'
if hw_zs not in skip_steps:
# Calculate zonal stats
zonal_stats_inputs = {
k: {
'path': v,
'stats': hw_config[zonal_stats][zs_stats]
}
for k, v in inputs.items()
if k in hw_config[zonal_stats][zs_rasters]
}
if bands_k in hw_config[zonal_stats].keys():
zonal_stats_inputs[img_k][bands_k] = hw_config[zonal_stats][
bands_k]
hw_objects = calc_zonal_stats(shp=cleaned_objects_out,
rasters=zonal_stats_inputs,
out_path=hw_zs_out)
else:
logger.debug('Using provided headwall objects with zonal stats: '
'{}'.format(Path(hw_zs_out).relative_to(project_dir)))
# hw_objects = hw_zs_out
# %%
# RTS
logger.info('\n\n***RTS***')
# Naming
rts_config[seg][params][img_k] = inputs[img_k]
# rts_config[seg][params][out_dir] = RTS_DIR
rts_seg_out = rts_config[seg][params][out_seg] = str(RTS_SEG_GPKG /
'rts_seg')
# Segmentation
if rts_config[seg][alg] == grm:
logger.info('Segmenting superobjects (RTS)...')
if rts_seg not in skip_steps:
rts_objects = otb_grm.otb_grm(drop_smaller=0.5,
**rts_config[seg][params])
else:
# rts_objects = otb_grm.create_outname(**rts_config[seg][params],
# name_only=True)
logger.debug('Using provided RTS seg: '
'{}'.format(
Path(rts_config[seg][params]
[out_seg]).relative_to(project_dir)))
# rts_objects = Path(rts_objects)
# %% Cleanup
# cleaned_objects_out = str(rts_objects.parent / '{}_cln{}'.format(
# rts_objects.stem, rts_objects.suffix))
cleaned_objects_out = rts_seg_out + '_cleaned'
if rts_clean not in skip_steps:
if rts_config[cleanup][cleanup]:
logger.info('Cleaning up objects...')
cleanup_params = rts_config[cleanup][params]
cleanup_params[mask_on] = str(inputs[dem_k])
# rts_objects = Path(rts_objects)
rts_objects = cleanup_objects(input_objects=rts_seg_out,
out_objects=cleaned_objects_out,
**cleanup_params)
else:
logger.debug('Using provided cleaned RTS objects: '
'{}'.format(
Path(cleaned_objects_out).relative_to(project_dir)))
# rts_objects = cleaned_objects_out
# %% Zonal Stats
logger.info('Calculating zonal statistics on super objects...')
# rts_objects_path = Path(rts_objects)
# rts_zs_out_path = '{}_zs{}'.format(rts_objects_path.parent /
# rts_objects_path.stem,
# rts_objects_path.suffix)
rts_zs_out = cleaned_objects_out + '_zs'
if rts_zs not in skip_steps:
# Calculate zonal_stats
zonal_stats_inputs = {
k: {
'path': v,
'stats': rts_config[zonal_stats][zs_stats]
}
for k, v in inputs.items()
if k in rts_config[zonal_stats][zs_rasters]
}
rts_objects = calc_zonal_stats(shp=cleaned_objects_out,
rasters=zonal_stats_inputs,
out_path=rts_zs_out)
else:
logger.debug('Using provided RTS zonal stats objects: '
'{}'.format(Path(rts_zs_out).relative_to(project_dir)))
# rts_objects = rts_zs_out_path
# %% CLASSIFICATION
if hw_config[classification_k][hw_class_out_k]:
# hw_class_out = CLASS_DIR / '{}{}'.format(hw_class_out_k, out_vec_fmt)
hw_class_out = CLASS_GPKG / 'headwalls'
if hw_config[classification_k][hw_class_out_cent_k]:
# hw_class_out_centroid = CLASS_DIR / '{}_cent{}'.format(hw_class_out_k,
# out_vec_fmt)
hw_class_out_centroid = CLASS_GPKG / 'headwall_centers'
# Pass path to classified headwall objects if using previously classified
if hw_class in skip_steps:
hw_candidates_in = hw_class_out
else:
hw_candidates_in = None
if rts_config[classification_k][rts_predis_out_k]:
# rts_predis_out = CLASS_DIR / '{}{}'.format(rts_predis_out_k, out_vec_fmt)
rts_predis_out = CLASS_GPKG / 'rts_predissolve'
if rts_config[classification_k][rts_class_out_k]:
# rts_class_out = CLASS_DIR / '{}{}'.format(rts_class_out_k, out_vec_fmt)
rts_class_out = CLASS_GPKG / 'rts_candidates'
if rts_class not in skip_steps:
logger.info('Classifying RTS...')
rts_objects = classify_rts(
sub_objects_path=hw_zs_out,
super_objects_path=rts_zs_out,
headwall_candidates_out=hw_class_out,
headwall_candidates_centroid_out=hw_class_out_centroid,
rts_predis_out=rts_predis_out,
rts_candidates_out=rts_class_out,
aoi_path=None,
headwall_candidates_in=hw_candidates_in,
aoi=aoi)
else:
logger.debug('Using provided classified RTS objects: '
'{}'.format(Path(rts_class_out).relative_to(project_dir)))
rts_objects = rts_class_out
#%% GROW OBJECTS
logger.info('\n\n***GROWING***')
logger.info('Creating grow subobjects..')
# Segment AOI into simple grow
grow_config[seg][params][img_k] = inputs[img_k]
# grow_config[seg][params][out_dir] = GROW_DIR
grow_seg_out = grow_config[seg][params][out_seg] = str(GROW_SEG_GPKG /
'grow')
if grow_seg not in skip_steps:
grow = otb_grm.otb_grm(drop_smaller=0.5, **grow_config[seg][params])
else:
grow = otb_grm.create_outname(**grow_config[seg][params],
name_only=True)
logger.debug('Using provided grow objects: '
'{}'.format(Path(grow).relative_to(project_dir)))
# Cleanup
# grow = Path(grow)
# cleaned_grow = str(grow.parent / '{}_cln{}'.format(grow.stem, grow.suffix))
cleaned_grow_out = grow_config[seg][params][out_seg] + '_cleaned'
if grow_clean not in skip_steps:
if grow_config[cleanup][cleanup]:
logger.info('Cleaning up objects...')
cleanup_params = grow_config[cleanup][params]
cleanup_params[mask_on] = str(inputs[dem_k])
cleaned_grow = cleanup_objects(input_objects=grow_seg_out,
out_objects=cleaned_grow_out,
**cleanup_params)
grow_zs_out = cleaned_grow_out + '_zs'
if grow_zs not in skip_steps:
logger.info('Merging RTS candidates into grow objects...')
# Load small objects
logger.debug(cleaned_grow_out)
# so = gpd.read_file(cleaned_grow_out)
so = read_vec(cleaned_grow_out)
# Burn rts in, including class name
logger.debug(rts_objects)
# r = gpd.read_file(rts_objects)
r = read_vec(rts_objects)
r = r[r[class_fld] == rts_candidate][[class_fld, r.geometry.name]]
# Erase subobjects under RTS candidates
diff = gpd.overlay(so, r, how='difference')
# Merge RTS candidates back in
merged = pd.concat([diff, r])
# merged_out = SEG_DIR / GROW_DIR / 'merged.shp'
merged_out = GROW_SEG_GPKG / 'merged'
write_gdf(merged, merged_out)
# Zonal Stats
zonal_stats_inputs = {
k: {
'path': v,
'stats': grow_config[zonal_stats][zs_stats]
}
for k, v in inputs.items()
if k in grow_config[zonal_stats][zs_rasters]
}
logger.info('Calculating zonal statistics on grow objects...')
logger.debug('Computing zonal statistics on: '
'{}'.format(zonal_stats_inputs.keys()))
grow = calc_zonal_stats(shp=merged_out,
rasters=zonal_stats_inputs,
out_path=str(grow_zs_out))
# Do growing
logger.info('Growing RTS objects into subobjects...')
# Grow from rts
# grow_objects = ImageObjects(grow_zs_out_path,
# value_fields=zonal_stats_inputs)
# # TODO: Remove after converting to use gpkg. This is just because of the
# # shapefile field size limit
# grow_objects.objects.rename(columns={'ruggedness': 'ruggedness_mean'},
# inplace=True)
#
# rts_objects = ImageObjects(rts_objects)
# grown = grow_rts_candidates(rts_objects, grow_objects)
grown, grow_candidates = grow_rts_simple(grow_zs_out)
grow_candidates_out = CLASS_GPKG / 'grow_candidates'
# logger.info('Writing grow objects to file: {}'.format(CLASS_DIR / 'grow_candidates.shp'))
logger.info('Writing grow objects to file: {}'.format(grow_candidates_out))
write_gdf(grow_candidates.objects, grow_candidates_out)
# n = datetime.now().strftime('%Y%b%d_%H%M%S').lower()
# rts_classified = CLASS_DIR / 'RTS.shp'
rts_classified = CLASS_GPKG / 'RTS'
logger.info('Writing classfied RTS features: {}'.format(rts_classified))
write_gdf(grown, rts_classified)
logger.info('Done')
def clip_rasters(shp_p, rasters, out_path=None, out_dir=None, out_suffix='_clip',
out_prj_shp=None, raster_ext=None, move_meta=False,
in_mem=False, skip_srs_check=False, overwrite=False):
"""
Take a list of rasters and warps (clips) them to the shapefile feature
bounding box.
rasters : LIST or STR
List of rasters to clip, or if STR, path to single raster.
out_prj_shp : os.path.abspath
Path to create the projected shapefile if necessary to match raster prj
"""
# TODO: Fix permission error if out_prj_shp not supplied -- create in-mem
# OGR?
# TODO: Add support for other vector formats -- create read_vec()??
# Use in memory directory if specified
if out_dir is None:
in_mem = True
if in_mem:
out_dir = r'/vsimem'
# Check that spatial references match, if not reproject (assumes all rasters have same projection)
# TODO: support different extension (slow to check all of them in the loop below)
# Check if list of rasters provided or if single raster
if isinstance(rasters, list):
check_raster = rasters[0]
else:
check_raster = rasters
rasters = [rasters]
if not skip_srs_check:
logger.debug('Checking spatial reference match:\n{}\n{}'.format(shp_p, check_raster))
sr_match = check_sr(shp_p, check_raster)
if not sr_match:
logger.debug('Spatial references do not match. Reprojecting to AOI...')
if not out_prj_shp:
out_prj_shp = shp_p.replace('.shp', '_prj.shp')
shp_p = ogr_reproject(shp_p,
to_sr=get_raster_sr(check_raster),
output_shp=out_prj_shp)
# shp = gpd.read_file(shp_p)
_shp_driver, shp_layer = detect_ogr_driver(shp_p)
shp = read_vec(shp_p)
if len(shp) > 1:
logger.debug('Dissolving clipping shape with multiple features...')
shp['dissolve'] = 1
shp = shp.dissolve(by='dissolve')
shp_p = r'/vsimem/clip_shp_dissolve.shp'
shp.to_file(shp_p)
# Do the 'warping' / clipping
warped = []
for raster_p in rasters:
# TODO: Handle this with platform.sys and pathlib.Path objects
raster_p = raster_p.replace(r'\\', os.sep)
raster_p = raster_p.replace(r'/', os.sep)
# Create out_path if not provided
if not out_path:
if not out_dir:
logger.debug('NO OUT_DIR')
# Create outpath
raster_out_name = '{}{}.tif'.format(
os.path.basename(raster_p).split('.')[0], out_suffix)
raster_out_path = os.path.join(out_dir, raster_out_name)
else:
raster_out_path = out_path
# Clip to shape
logger.info('Clipping:\n{}\n\t---> '
'{}'.format(os.path.basename(raster_p),
raster_out_path))
if os.path.exists(raster_out_path) and not overwrite:
logger.warning('Outpath exists, skipping: '
'{}'.format(raster_out_path))
pass
else:
raster_ds = gdal.Open(raster_p, gdal.GA_ReadOnly)
x_res = raster_ds.GetGeoTransform()[1]
y_res = raster_ds.GetGeoTransform()[5]
if shp_layer is not None:
warp_options = gdal.WarpOptions(cutlineDSName=Path(shp_p).parent,
cutlineLayer=shp_layer,
cropToCutline=True,
targetAlignedPixels=True,
xRes=x_res,
yRes=y_res)
else:
warp_options = gdal.WarpOptions(cutlineDSName=shp_p,
cropToCutline=True,
targetAlignedPixels=True,
xRes=x_res,
yRes=y_res)
gdal.Warp(raster_out_path, raster_ds, options=warp_options)
# Close the raster
raster_ds = None
logger.debug('Clipped raster created at {}'.format(raster_out_path))
# Add clipped raster path to list of clipped rasters to return
warped.append(raster_out_path)
# Move meta-data files if specified
if move_meta:
logger.debug('Moving metadata files to clip destination...')
move_meta_files(raster_p, out_dir, raster_ext=raster_ext)
# Remove projected shp
if in_mem is True:
remove_shp(out_prj_shp)
# If only one raster provided, just return the single path as str
if len(warped) == 1:
warped = warped[0]
return warped
