def process_vector_label(rst_pth, gpkg_pth, ids):
if rst_pth is not None:
with rasterio.open(rst_pth) as src:
np_label = vector_to_raster(
vector_file=gpkg_pth,
input_image=src,
out_shape=(src.height, src.width),
attribute_name='properties/Quatreclasses',
fill=0,
target_ids=ids,
merge_all=True,
)
return np_label
def main(params):
"""
Training and validation datasets preparation.
Process
-------
1. Read csv file and validate existence of all input files and GeoPackages.
2. Do the following verifications:
1. Assert number of bands found in raster is equal to desired number
of bands.
2. Check that `num_classes` is equal to number of classes detected in
the specified attribute for each GeoPackage.
Warning: this validation will not succeed if a Geopackage
contains only a subset of `num_classes` (e.g. 3 of 4).
3. Assert Coordinate reference system between raster and gpkg match.
3. Read csv file and for each line in the file, do the following:
1. Read input image as array with utils.readers.image_reader_as_array().
- If gpkg's extent is smaller than raster's extent,
raster is clipped to gpkg's extent.
- If gpkg's extent is bigger than raster's extent,
gpkg is clipped to raster's extent.
2. Convert GeoPackage vector information into the "label" raster with
utils.utils.vector_to_raster(). The pixel value is determined by the
attribute in the csv file.
3. Create a new raster called "label" with the same properties as the
input image.
4. Read metadata and add to input as new bands (*more details to come*).
5. Crop the arrays in smaller samples of the size `samples_size` of
`your_conf.yaml`. Visual representation of this is provided at
https://medium.com/the-downlinq/broad-area-satellite-imagery-semantic-segmentation-basiss-4a7ea2c8466f
6. Write samples from input image and label into the "val", "trn" or
"tst" hdf5 file, depending on the value contained in the csv file.
Refer to samples_preparation().
-------
:param params: (dict) Parameters found in the yaml config file.
"""
params['global']['git_hash'] = get_git_hash()
now = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M")
bucket_file_cache = []
assert params['global'][
'task'] == 'segmentation', f"images_to_samples.py isn't necessary when performing classification tasks"
# SET BASIC VARIABLES AND PATHS. CREATE OUTPUT FOLDERS.
bucket_name = get_key_def('bucket_name', params['global'])
data_path = Path(params['global']['data_path'])
Path.mkdir(data_path, exist_ok=True, parents=True)
csv_file = params['sample']['prep_csv_file']
val_percent = params['sample']['val_percent']
samples_size = params["global"]["samples_size"]
overlap = params["sample"]["overlap"]
min_annot_perc = get_key_def('min_annotated_percent',
params['sample']['sampling_method'],
None,
expected_type=int)
num_bands = params['global']['number_of_bands']
debug = get_key_def('debug_mode', params['global'], False)
if debug:
warnings.warn(f'Debug mode activate. Execution may take longer...')
final_samples_folder = None
sample_path_name = f'samples{samples_size}_overlap{overlap}_min-annot{min_annot_perc}_{num_bands}bands'
# AWS
if bucket_name:
s3 = boto3.resource('s3')
bucket = s3.Bucket(bucket_name)
bucket.download_file(csv_file, 'samples_prep.csv')
list_data_prep = read_csv('samples_prep.csv')
if data_path:
final_samples_folder = data_path.joinpath("samples")
else:
final_samples_folder = "samples"
samples_folder = sample_path_name
else:
list_data_prep = read_csv(csv_file)
samples_folder = data_path.joinpath(sample_path_name)
if samples_folder.is_dir():
warnings.warn(
f'Data path exists: {samples_folder}. Suffix will be added to directory name.'
)
samples_folder = Path(str(samples_folder) + '_' + now)
else:
tqdm.write(f'Writing samples to {samples_folder}')
Path.mkdir(samples_folder, exist_ok=False
) # TODO: what if we want to append samples to existing hdf5?
tqdm.write(f'Samples will be written to {samples_folder}\n\n')
tqdm.write(f'\nSuccessfully read csv file: {Path(csv_file).stem}\n'
f'Number of rows: {len(list_data_prep)}\n'
f'Copying first entry:\n{list_data_prep[0]}\n')
ignore_index = get_key_def('ignore_index', params['training'], -1)
meta_map, metadata = get_key_def("meta_map", params["global"], {}), None
# VALIDATION: (1) Assert num_classes parameters == num actual classes in gpkg and (2) check CRS match (tif and gpkg)
valid_gpkg_set = set()
for info in tqdm(list_data_prep, position=0):
assert_num_bands(info['tif'], num_bands, meta_map)
if info['gpkg'] not in valid_gpkg_set:
gpkg_classes = validate_num_classes(
info['gpkg'], params['global']['num_classes'],
info['attribute_name'], ignore_index)
assert_crs_match(info['tif'], info['gpkg'])
valid_gpkg_set.add(info['gpkg'])
if debug:
# VALIDATION (debug only): Checking validity of features in vector files
for info in tqdm(
list_data_prep,
position=0,
desc=f"Checking validity of features in vector files"):
invalid_features = validate_features_from_gpkg(
info['gpkg'], info['attribute_name']
) # TODO: test this with invalid features.
assert not invalid_features, f"{info['gpkg']}: Invalid geometry object(s) '{invalid_features}'"
number_samples = {'trn': 0, 'val': 0, 'tst': 0}
number_classes = 0
class_prop = get_key_def('class_proportion',
params['sample']['sampling_method'],
None,
expected_type=dict)
trn_hdf5, val_hdf5, tst_hdf5 = create_files_and_datasets(
params, samples_folder)
# Set dontcare (aka ignore_index) value
dontcare = get_key_def(
"ignore_index", params["training"],
-1) # TODO: deduplicate with train_segmentation, l300
if dontcare == 0:
warnings.warn(
"The 'dontcare' value (or 'ignore_index') used in the loss function cannot be zero;"
" all valid class indices should be consecutive, and start at 0. The 'dontcare' value"
" will be remapped to -1 while loading the dataset, and inside the config from now on."
)
params["training"]["ignore_index"] = -1
# creates pixel_classes dict and keys
pixel_classes = {key: 0 for key in gpkg_classes}
background_val = 0
pixel_classes[background_val] = 0
class_prop = validate_class_prop_dict(pixel_classes, class_prop)
pixel_classes[dontcare] = 0
# For each row in csv: (1) burn vector file to raster, (2) read input raster image, (3) prepare samples
with tqdm(list_data_prep,
position=0,
leave=False,
desc=f'Preparing samples') as _tqdm:
for info in _tqdm:
_tqdm.set_postfix(
OrderedDict(tif=f'{Path(info["tif"]).stem}',
sample_size=params['global']['samples_size']))
try:
if bucket_name:
bucket.download_file(
info['tif'], "Images/" + info['tif'].split('/')[-1])
info['tif'] = "Images/" + info['tif'].split('/')[-1]
if info['gpkg'] not in bucket_file_cache:
bucket_file_cache.append(info['gpkg'])
bucket.download_file(info['gpkg'],
info['gpkg'].split('/')[-1])
info['gpkg'] = info['gpkg'].split('/')[-1]
if info['meta']:
if info['meta'] not in bucket_file_cache:
bucket_file_cache.append(info['meta'])
bucket.download_file(info['meta'],
info['meta'].split('/')[-1])
info['meta'] = info['meta'].split('/')[-1]
with rasterio.open(info['tif'], 'r') as raster:
# 1. Read the input raster image
np_input_image, raster, dataset_nodata = image_reader_as_array(
input_image=raster,
clip_gpkg=info['gpkg'],
aux_vector_file=get_key_def('aux_vector_file',
params['global'], None),
aux_vector_attrib=get_key_def('aux_vector_attrib',
params['global'], None),
aux_vector_ids=get_key_def('aux_vector_ids',
params['global'], None),
aux_vector_dist_maps=get_key_def(
'aux_vector_dist_maps', params['global'], True),
aux_vector_dist_log=get_key_def(
'aux_vector_dist_log', params['global'], True),
aux_vector_scale=get_key_def('aux_vector_scale',
params['global'], None))
# 2. Burn vector file in a raster file
np_label_raster = vector_to_raster(
vector_file=info['gpkg'],
input_image=raster,
out_shape=np_input_image.shape[:2],
attribute_name=info['attribute_name'],
fill=background_val
) # background value in rasterized vector.
if dataset_nodata is not None:
# 3. Set ignore_index value in label array where nodata in raster (only if nodata across all bands)
np_label_raster[dataset_nodata] = dontcare
if debug:
out_meta = raster.meta.copy()
np_image_debug = np_input_image.transpose(2, 0, 1).astype(
out_meta['dtype'])
out_meta.update({
"driver": "GTiff",
"height": np_image_debug.shape[1],
"width": np_image_debug.shape[2]
})
out_tif = samples_folder / f"np_input_image_{_tqdm.n}.tif"
print(f"DEBUG: writing clipped raster to {out_tif}")
with rasterio.open(out_tif, "w", **out_meta) as dest:
dest.write(np_image_debug)
out_meta = raster.meta.copy()
np_label_debug = np.expand_dims(
np_label_raster,
axis=2).transpose(2, 0, 1).astype(out_meta['dtype'])
out_meta.update({
"driver": "GTiff",
"height": np_label_debug.shape[1],
"width": np_label_debug.shape[2],
'count': 1
})
out_tif = samples_folder / f"np_label_rasterized_{_tqdm.n}.tif"
print(f"DEBUG: writing final rasterized gpkg to {out_tif}")
with rasterio.open(out_tif, "w", **out_meta) as dest:
dest.write(np_label_debug)
# Mask the zeros from input image into label raster.
if params['sample']['mask_reference']:
np_label_raster = mask_image(np_input_image,
np_label_raster)
if info['dataset'] == 'trn':
out_file = trn_hdf5
elif info['dataset'] == 'tst':
out_file = tst_hdf5
else:
raise ValueError(
f"Dataset value must be trn or tst. Provided value is {info['dataset']}"
)
val_file = val_hdf5
metadata = add_metadata_from_raster_to_sample(
sat_img_arr=np_input_image,
raster_handle=raster,
meta_map=meta_map,
raster_info=info)
# Save label's per class pixel count to image metadata
metadata['source_label_bincount'] = {
class_num: count
for class_num, count in enumerate(
np.bincount(np_label_raster.clip(min=0).flatten()))
if count > 0
} # TODO: add this to add_metadata_from[...] function?
np_label_raster = np.reshape(
np_label_raster,
(np_label_raster.shape[0], np_label_raster.shape[1], 1))
# 3. Prepare samples!
number_samples, number_classes = samples_preparation(
in_img_array=np_input_image,
label_array=np_label_raster,
sample_size=samples_size,
overlap=overlap,
samples_count=number_samples,
num_classes=number_classes,
samples_file=out_file,
val_percent=val_percent,
val_sample_file=val_file,
dataset=info['dataset'],
pixel_classes=pixel_classes,
image_metadata=metadata,
dontcare=dontcare,
min_annot_perc=min_annot_perc,
class_prop=class_prop)
_tqdm.set_postfix(OrderedDict(number_samples=number_samples))
out_file.flush()
except OSError as e:
warnings.warn(
f'An error occurred while preparing samples with "{Path(info["tif"]).stem}" (tiff) and '
f'{Path(info["gpkg"]).stem} (gpkg). Error: "{e}"')
continue
trn_hdf5.close()
val_hdf5.close()
tst_hdf5.close()
pixel_total = 0
# adds up the number of pixels for each class in pixel_classes dict
for i in pixel_classes:
pixel_total += pixel_classes[i]
# prints the proportion of pixels of each class for the samples created
for i in pixel_classes:
prop = round((pixel_classes[i] / pixel_total) *
100, 1) if pixel_total > 0 else 0
print('Pixels from class', i, ':', prop, '%')
print("Number of samples created: ", number_samples)
if bucket_name and final_samples_folder:
print('Transfering Samples to the bucket')
bucket.upload_file(samples_folder + "/trn_samples.hdf5",
final_samples_folder + '/trn_samples.hdf5')
bucket.upload_file(samples_folder + "/val_samples.hdf5",
final_samples_folder + '/val_samples.hdf5')
bucket.upload_file(samples_folder + "/tst_samples.hdf5",
final_samples_folder + '/tst_samples.hdf5')
print("End of process")
def main(params: dict):
"""
Identify the class to which each image belongs.
:param params: (dict) Parameters found in the yaml config file.
"""
since = time.time()
# MANDATORY PARAMETERS
img_dir_or_csv = get_key_def('img_dir_or_csv_file',
params['inference'],
expected_type=str)
state_dict = get_key_def('state_dict_path', params['inference'])
task = get_key_def('task', params['global'], expected_type=str)
if task not in ['classification', 'segmentation']:
raise ValueError(
f'Task should be either "classification" or "segmentation". Got {task}'
)
model_name = get_key_def('model_name', params['global'],
expected_type=str).lower()
num_classes = get_key_def('num_classes',
params['global'],
expected_type=int)
num_bands = get_key_def('number_of_bands',
params['global'],
expected_type=int)
chunk_size = get_key_def('chunk_size',
params['inference'],
default=512,
expected_type=int)
BGR_to_RGB = get_key_def('BGR_to_RGB',
params['global'],
expected_type=bool)
# OPTIONAL PARAMETERS
dontcare_val = get_key_def("ignore_index",
params["training"],
default=-1,
expected_type=int)
num_devices = get_key_def('num_gpus',
params['global'],
default=0,
expected_type=int)
default_max_used_ram = 25
max_used_ram = get_key_def('max_used_ram',
params['global'],
default=default_max_used_ram,
expected_type=int)
max_used_perc = get_key_def('max_used_perc',
params['global'],
default=25,
expected_type=int)
scale = get_key_def('scale_data',
params['global'],
default=[0, 1],
expected_type=List)
debug = get_key_def('debug_mode',
params['global'],
default=False,
expected_type=bool)
raster_to_vec = get_key_def('ras2vec', params['inference'], False)
# benchmark (ie when gkpgs are inputted along with imagery)
dontcare = get_key_def("ignore_index", params["training"], -1)
targ_ids = get_key_def('target_ids',
params['sample'],
None,
expected_type=List)
# SETTING OUTPUT DIRECTORY
working_folder = Path(
params['inference']['state_dict_path']).parent.joinpath(
f'inference_{num_bands}bands')
Path.mkdir(working_folder, parents=True, exist_ok=True)
# mlflow logging
mlflow_uri = get_key_def('mlflow_uri',
params['global'],
default=None,
expected_type=str)
if mlflow_uri and not Path(mlflow_uri).is_dir():
warnings.warn(f'Mlflow uri path is not valid: {mlflow_uri}')
mlflow_uri = None
# SETUP LOGGING
import logging.config # See: https://docs.python.org/2.4/lib/logging-config-fileformat.html
if mlflow_uri:
log_config_path = Path('utils/logging.conf').absolute()
logfile = f'{working_folder}/info.log'
logfile_debug = f'{working_folder}/debug.log'
console_level_logging = 'INFO' if not debug else 'DEBUG'
logging.config.fileConfig(log_config_path,
defaults={
'logfilename': logfile,
'logfilename_debug': logfile_debug,
'console_level': console_level_logging
})
# import only if mlflow uri is set
from mlflow import log_params, set_tracking_uri, set_experiment, start_run, log_artifact, log_metrics
if not Path(mlflow_uri).is_dir():
logging.warning(
f"Couldn't locate mlflow uri directory {mlflow_uri}. Directory will be created."
)
Path(mlflow_uri).mkdir()
set_tracking_uri(mlflow_uri)
exp_name = get_key_def('mlflow_experiment_name',
params['global'],
default='gdl-inference',
expected_type=str)
set_experiment(f'{exp_name}/{working_folder.name}')
run_name = get_key_def('mlflow_run_name',
params['global'],
default='gdl',
expected_type=str)
start_run(run_name=run_name)
log_params(params['global'])
log_params(params['inference'])
else:
# set a console logger as default
logging.basicConfig(level=logging.DEBUG)
logging.info(
'No logging folder set for mlflow. Logging will be limited to console'
)
if debug:
logging.warning(
f'Debug mode activated. Some debug features may mobilize extra disk space and '
f'cause delays in execution.')
# Assert that all items in target_ids are integers (ex.: to benchmark single-class model with multi-class labels)
if targ_ids:
for item in targ_ids:
if not isinstance(item, int):
raise ValueError(
f'Target id "{item}" in target_ids is {type(item)}, expected int.'
)
logging.info(f'Inferences will be saved to: {working_folder}\n\n')
if not (0 <= max_used_ram <= 100):
logging.warning(
f'Max used ram parameter should be a percentage. Got {max_used_ram}. '
f'Will set default value of {default_max_used_ram} %')
max_used_ram = default_max_used_ram
# AWS
bucket = None
bucket_file_cache = []
bucket_name = get_key_def('bucket_name', params['global'])
# list of GPU devices that are available and unused. If no GPUs, returns empty dict
gpu_devices_dict = get_device_ids(num_devices,
max_used_ram_perc=max_used_ram,
max_used_perc=max_used_perc)
if gpu_devices_dict:
logging.info(
f"Number of cuda devices requested: {num_devices}. Cuda devices available: {gpu_devices_dict}. "
f"Using {list(gpu_devices_dict.keys())[0]}\n\n")
device = torch.device(
f'cuda:{list(range(len(gpu_devices_dict.keys())))[0]}')
else:
logging.warning(
f"No Cuda device available. This process will only run on CPU")
device = torch.device('cpu')
# CONFIGURE MODEL
num_classes_backgr = add_background_to_num_class(task, num_classes)
model, loaded_checkpoint, model_name = net(model_name=model_name,
num_bands=num_bands,
num_channels=num_classes_backgr,
dontcare_val=dontcare_val,
num_devices=1,
net_params=params,
inference_state_dict=state_dict)
try:
model.to(device)
except RuntimeError:
logging.info(f"Unable to use device 0")
device = torch.device(f'cuda' if gpu_devices_dict else 'cpu')
model.to(device)
# CREATE LIST OF INPUT IMAGES FOR INFERENCE
list_img = list_input_images(img_dir_or_csv,
bucket_name,
glob_patterns=["*.tif", "*.TIF"])
# VALIDATION: anticipate problems with imagery and label (if provided) before entering main for loop
valid_gpkg_set = set()
for info in tqdm(list_img, desc='Validating imagery'):
# validate_raster(info['tif'], num_bands, meta_map)
if 'gpkg' in info.keys(
) and info['gpkg'] and info['gpkg'] not in valid_gpkg_set:
validate_num_classes(vector_file=info['gpkg'],
num_classes=num_classes,
attribute_name=info['attribute_name'],
ignore_index=dontcare,
target_ids=targ_ids)
assert_crs_match(info['tif'], info['gpkg'])
valid_gpkg_set.add(info['gpkg'])
logging.info('Successfully validated imagery')
if valid_gpkg_set:
logging.info('Successfully validated label data for benchmarking')
if task == 'classification':
classifier(
params, list_img, model, device, working_folder
) # FIXME: why don't we load from checkpoint in classification?
elif task == 'segmentation':
gdf_ = []
gpkg_name_ = []
# TODO: Add verifications?
if bucket:
bucket.download_file(
loaded_checkpoint,
"saved_model.pth.tar") # TODO: is this still valid?
model, _ = load_from_checkpoint("saved_model.pth.tar", model)
else:
model, _ = load_from_checkpoint(loaded_checkpoint, model)
# LOOP THROUGH LIST OF INPUT IMAGES
for info in tqdm(list_img,
desc='Inferring from images',
position=0,
leave=True):
with start_run(run_name=Path(info['tif']).name, nested=True):
img_name = Path(info['tif']).name
local_gpkg = Path(
info['gpkg']
) if 'gpkg' in info.keys() and info['gpkg'] else None
gpkg_name = local_gpkg.stem if local_gpkg else None
if bucket:
local_img = f"Images/{img_name}"
bucket.download_file(info['tif'], local_img)
inference_image = f"Classified_Images/{img_name.split('.')[0]}_inference.tif"
if info['meta']:
if info['meta'] not in bucket_file_cache:
bucket_file_cache.append(info['meta'])
bucket.download_file(info['meta'],
info['meta'].split('/')[-1])
info['meta'] = info['meta'].split('/')[-1]
else: # FIXME: else statement should support img['meta'] integration as well.
local_img = Path(info['tif'])
Path.mkdir(working_folder.joinpath(local_img.parent.name),
parents=True,
exist_ok=True)
inference_image = working_folder.joinpath(
local_img.parent.name,
f"{img_name.split('.')[0]}_inference.tif")
temp_file = working_folder.joinpath(
local_img.parent.name, f"{img_name.split('.')[0]}.dat")
raster = rasterio.open(local_img, 'r')
logging.info(f'Reading original image: {raster.name}')
inf_meta = raster.meta
label = None
if local_gpkg:
logging.info(f'Burning label as raster: {local_gpkg}')
local_img = clip_raster_with_gpkg(raster, local_gpkg)
raster.close()
raster = rasterio.open(local_img, 'r')
logging.info(f'Reading clipped image: {raster.name}')
inf_meta = raster.meta
label = vector_to_raster(
vector_file=local_gpkg,
input_image=raster,
out_shape=(inf_meta['height'], inf_meta['width']),
attribute_name=info['attribute_name'],
fill=0, # background value in rasterized vector.
target_ids=targ_ids)
if debug:
logging.debug(
f'Unique values in loaded label as raster: {np.unique(label)}\n'
f'Shape of label as raster: {label.shape}')
pred, gdf = segmentation(param=params,
input_image=raster,
label_arr=label,
num_classes=num_classes_backgr,
gpkg_name=gpkg_name,
model=model,
chunk_size=chunk_size,
device=device,
scale=scale,
BGR_to_RGB=BGR_to_RGB,
tp_mem=temp_file,
debug=debug)
if gdf is not None:
gdf_.append(gdf)
gpkg_name_.append(gpkg_name)
if local_gpkg:
pixelMetrics = ComputePixelMetrics(label, pred,
num_classes_backgr)
log_metrics(pixelMetrics.update(pixelMetrics.iou))
log_metrics(pixelMetrics.update(pixelMetrics.dice))
pred = pred[np.newaxis, :, :].astype(np.uint8)
inf_meta.update({
"driver": "GTiff",
"height": pred.shape[1],
"width": pred.shape[2],
"count": pred.shape[0],
"dtype": 'uint8',
"compress": 'lzw'
})
logging.info(
f'Successfully inferred on {img_name}\nWriting to file: {inference_image}'
)
with rasterio.open(inference_image, 'w+', **inf_meta) as dest:
dest.write(pred)
del pred
try:
temp_file.unlink()
except OSError as e:
logging.warning(f'File Error: {temp_file, e.strerror}')
if raster_to_vec:
start_vec = time.time()
inference_vec = working_folder.joinpath(
local_img.parent.name,
f"{img_name.split('.')[0]}_inference.gpkg")
ras2vec(inference_image, inference_vec)
end_vec = time.time() - start_vec
logging.info(
'Vectorization completed in {:.0f}m {:.0f}s'.format(
end_vec // 60, end_vec % 60))
if len(gdf_) >= 1:
if not len(gdf_) == len(gpkg_name_):
raise ValueError('benchmarking unable to complete')
all_gdf = pd.concat(
gdf_) # Concatenate all geo data frame into one geo data frame
all_gdf.reset_index(drop=True, inplace=True)
gdf_x = gpd.GeoDataFrame(all_gdf)
bench_gpkg = working_folder / "benchmark.gpkg"
gdf_x.to_file(bench_gpkg, driver="GPKG", index=False)
logging.info(
f'Successfully wrote benchmark geopackage to: {bench_gpkg}')
# log_artifact(working_folder)
time_elapsed = time.time() - since
logging.info('Inference Script completed in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
def create_csv():
"""
Creates samples from the input images for the pixel_inventory function
"""
prep_csv_path = params['sample']['prep_csv_file']
dist_samples = params['sample']['samples_dist']
sample_size = params['global']['samples_size']
data_path = params['global']['data_path']
Path.mkdir(Path(data_path), exist_ok=True)
num_classes = params['global']['num_classes']
data_prep_csv = read_csv(prep_csv_path)
csv_prop_data = params['global']['data_path'] + '/prop_data.csv'
if os.path.isfile(csv_prop_data):
os.remove(csv_prop_data)
with tqdm(data_prep_csv) as _tqdm:
for info in _tqdm:
_tqdm.set_postfix(OrderedDict(file=f'{info["tif"]}', sample_size=params['global']['samples_size']))
# Validate the number of class in the vector file
validate_num_classes(info['gpkg'], num_classes, info['attribute_name'])
assert os.path.isfile(info['tif']), f"could not open raster file at {info['tif']}"
with rasterio.open(info['tif'], 'r') as raster:
# Burn vector file in a raster file
np_label_raster = vector_to_raster(vector_file=info['gpkg'],
input_image=raster,
attribute_name=info['attribute_name'],
fill=get_key_def('ignore_idx', get_key_def('training', params, {}),
0))
# Read the input raster image
np_input_image = image_reader_as_array(input_image=raster,
aux_vector_file=get_key_def('aux_vector_file', params['global'],
None),
aux_vector_attrib=get_key_def('aux_vector_attrib',
params['global'], None),
aux_vector_ids=get_key_def('aux_vector_ids', params['global'],
None),
aux_vector_dist_maps=get_key_def('aux_vector_dist_maps',
params['global'], True),
aux_vector_dist_log=get_key_def('aux_vector_dist_log',
params['global'], True),
aux_vector_scale=get_key_def('aux_vector_scale',
params['global'], None))
# Mask the zeros from input image into label raster.
if params['sample']['mask_reference']:
np_label_raster = images_to_samples.mask_image(np_input_image, np_label_raster)
np_label_raster = np.reshape(np_label_raster, (np_label_raster.shape[0], np_label_raster.shape[1], 1))
h, w, num_bands = np_input_image.shape
# half tile padding
half_tile = int(sample_size / 2)
pad_label_array = np.pad(np_label_raster, ((half_tile, half_tile), (half_tile, half_tile), (0, 0)),
mode='constant')
for row in range(0, h, dist_samples):
for column in range(0, w, dist_samples):
target = np.squeeze(pad_label_array[row:row + sample_size, column:column + sample_size, :], axis=2)
pixel_inventory(target, sample_size, params['global']['num_classes'] + 1,
params['global']['data_path'], info['dataset'])
def main(params: dict) -> None:
"""
Function to manage details about the inference on segmentation task.
1. Read the parameters from the config given.
2. Read and load the state dict from the previous training or the given one.
3. Make the inference on the data specifies in the config.
-------
:param params: (dict) Parameters found in the yaml config file.
"""
# since = time.time()
# PARAMETERS
mode = get_key_def('mode', params, expected_type=str)
task = get_key_def('task_name', params['task'], expected_type=str)
model_name = get_key_def('model_name', params['model'], expected_type=str).lower()
num_classes = len(get_key_def('classes_dict', params['dataset']).keys())
modalities = read_modalities(get_key_def('modalities', params['dataset'], expected_type=str))
BGR_to_RGB = get_key_def('BGR_to_RGB', params['dataset'], expected_type=bool)
num_bands = len(modalities)
debug = get_key_def('debug', params, default=False, expected_type=bool)
# SETTING OUTPUT DIRECTORY
try:
state_dict = Path(params['inference']['state_dict_path']).resolve(strict=True)
except FileNotFoundError:
logging.info(
f"\nThe state dict path directory '{params['inference']['state_dict_path']}' don't seem to be find," +
f"we will try to locate a state dict path in the '{params['general']['save_weights_dir']}' " +
f"specify during the training phase"
)
try:
state_dict = Path(params['general']['save_weights_dir']).resolve(strict=True)
except FileNotFoundError:
raise logging.critical(
f"\nThe state dict path directory '{params['general']['save_weights_dir']}'" +
f" don't seem to be find either, please specify the path to a state dict"
)
# TODO add more detail in the parent folder
working_folder = state_dict.parent.joinpath(f'inference_{num_bands}bands')
logging.info("\nThe state dict path directory used '{}'".format(working_folder))
Path.mkdir(working_folder, parents=True, exist_ok=True)
# LOGGING PARAMETERS TODO put option not just mlflow
experiment_name = get_key_def('project_name', params['general'], default='gdl-training')
try:
tracker_uri = get_key_def('uri', params['tracker'], default=None, expected_type=str)
Path(tracker_uri).mkdir(exist_ok=True)
run_name = get_key_def('run_name', params['tracker'], default='gdl') # TODO change for something meaningful
run_name = '{}_{}_{}'.format(run_name, mode, task)
logging.info(f'\nInference and log files will be saved to: {working_folder}')
# TODO change to fit whatever inport
from mlflow import log_params, set_tracking_uri, set_experiment, start_run, log_artifact, log_metrics
# tracking path + parameters logging
set_tracking_uri(tracker_uri)
set_experiment(experiment_name)
start_run(run_name=run_name)
log_params(dict_path(params, 'general'))
log_params(dict_path(params, 'dataset'))
log_params(dict_path(params, 'data'))
log_params(dict_path(params, 'model'))
log_params(dict_path(params, 'inference'))
# meaning no logging tracker as been assigned or it doesnt exist in config/logging
except ConfigKeyError:
logging.info(
"\nNo logging tracker as been assigned or the yaml config doesnt exist in 'config/tracker'."
"\nNo tracker file will be save in that case."
)
# MANDATORY PARAMETERS
img_dir_or_csv = get_key_def(
'img_dir_or_csv_file', params['inference'], default=params['general']['raw_data_csv'], expected_type=str
)
if not (Path(img_dir_or_csv).is_dir() or Path(img_dir_or_csv).suffix == '.csv'):
raise logging.critical(
FileNotFoundError(
f'\nCouldn\'t locate .csv file or directory "{img_dir_or_csv}" containing imagery for inference'
)
)
# load the checkpoint
try:
# Sort by modification time (mtime) descending
sorted_by_mtime_descending = sorted(
[os.path.join(state_dict, x) for x in os.listdir(state_dict)], key=lambda t: -os.stat(t).st_mtime
)
last_checkpoint_save = find_first_file('checkpoint.pth.tar', sorted_by_mtime_descending)
if last_checkpoint_save is None:
raise FileNotFoundError
# change the state_dict
state_dict = last_checkpoint_save
except FileNotFoundError as e:
logging.error(f"\nNo file name 'checkpoint.pth.tar' as been found at '{state_dict}'")
raise e
task = get_key_def('task_name', params['task'], expected_type=str)
# TODO change it next version for all task
if task not in ['classification', 'segmentation']:
raise logging.critical(
ValueError(f'\nTask should be either "classification" or "segmentation". Got {task}')
)
# OPTIONAL PARAMETERS
dontcare_val = get_key_def("ignore_index", params["training"], default=-1, expected_type=int)
num_devices = get_key_def('num_gpus', params['training'], default=0, expected_type=int)
default_max_used_ram = 25
max_used_ram = get_key_def('max_used_ram', params['training'], default=default_max_used_ram, expected_type=int)
max_used_perc = get_key_def('max_used_perc', params['training'], default=25, expected_type=int)
scale = get_key_def('scale_data', params['augmentation'], default=[0, 1], expected_type=ListConfig)
raster_to_vec = get_key_def('ras2vec', params['inference'], False) # FIXME not implemented with hydra
# benchmark (ie when gkpgs are inputted along with imagery)
dontcare = get_key_def("ignore_index", params["training"], -1)
targ_ids = None # TODO get_key_def('target_ids', params['sample'], None, expected_type=List)
if debug:
logging.warning(f'\nDebug mode activated. Some debug features may mobilize extra disk space and '
f'cause delays in execution.')
# Assert that all items in target_ids are integers (ex.: to benchmark single-class model with multi-class labels)
if targ_ids:
for item in targ_ids:
if not isinstance(item, int):
raise ValueError(f'\nTarget id "{item}" in target_ids is {type(item)}, expected int.')
logging.info(f'\nInferences will be saved to: {working_folder}\n\n')
if not (0 <= max_used_ram <= 100):
logging.warning(f'\nMax used ram parameter should be a percentage. Got {max_used_ram}. '
f'Will set default value of {default_max_used_ram} %')
max_used_ram = default_max_used_ram
# AWS
bucket = None
bucket_file_cache = []
bucket_name = get_key_def('bucket_name', params['AWS'])
# list of GPU devices that are available and unused. If no GPUs, returns empty dict
gpu_devices_dict = get_device_ids(num_devices,
max_used_ram_perc=max_used_ram,
max_used_perc=max_used_perc)
if gpu_devices_dict:
chunk_size = calc_inference_chunk_size(gpu_devices_dict=gpu_devices_dict, max_pix_per_mb_gpu=50)
logging.info(f"\nNumber of cuda devices requested: {num_devices}. "
f"\nCuda devices available: {gpu_devices_dict}. "
f"\nUsing {list(gpu_devices_dict.keys())[0]}\n\n")
device = torch.device(f'cuda:{list(range(len(gpu_devices_dict.keys())))[0]}')
else:
chunk_size = get_key_def('chunk_size', params['inference'], default=512, expected_type=int)
logging.warning(f"\nNo Cuda device available. This process will only run on CPU")
device = torch.device('cpu')
# CONFIGURE MODEL
num_classes_backgr = add_background_to_num_class(task, num_classes)
model, loaded_checkpoint, model_name = net(model_name=model_name,
num_bands=num_bands,
num_channels=num_classes_backgr,
dontcare_val=dontcare_val,
num_devices=1,
net_params=params,
inference_state_dict=state_dict)
try:
model.to(device)
except RuntimeError:
logging.info(f"\nUnable to use device. Trying device 0")
device = torch.device(f'cuda' if gpu_devices_dict else 'cpu')
model.to(device)
# CREATE LIST OF INPUT IMAGES FOR INFERENCE
try:
# check if the data folder exist
raw_data_dir = get_key_def('raw_data_dir', params['dataset'])
my_data_path = Path(raw_data_dir).resolve(strict=True)
logging.info("\nImage directory used '{}'".format(my_data_path))
data_path = Path(my_data_path)
except FileNotFoundError:
raw_data_dir = get_key_def('raw_data_dir', params['dataset'])
raise logging.critical(
"\nImage directory '{}' doesn't exist, please change the path".format(raw_data_dir)
)
list_img = list_input_images(
img_dir_or_csv, bucket_name, glob_patterns=["*.tif", "*.TIF"], in_case_of_path=str(data_path)
)
# VALIDATION: anticipate problems with imagery and label (if provided) before entering main for loop
valid_gpkg_set = set()
for info in tqdm(list_img, desc='Validating imagery'):
# validate_raster(info['tif'], num_bands, meta_map)
if 'gpkg' in info.keys() and info['gpkg'] and info['gpkg'] not in valid_gpkg_set:
validate_num_classes(vector_file=info['gpkg'],
num_classes=num_classes,
attribute_name=info['attribute_name'],
ignore_index=dontcare,
target_ids=targ_ids)
assert_crs_match(info['tif'], info['gpkg'])
valid_gpkg_set.add(info['gpkg'])
logging.info('\nSuccessfully validated imagery')
if valid_gpkg_set:
logging.info('\nSuccessfully validated label data for benchmarking')
if task == 'classification':
classifier(params, list_img, model, device,
working_folder) # FIXME: why don't we load from checkpoint in classification?
elif task == 'segmentation':
gdf_ = []
gpkg_name_ = []
# TODO: Add verifications?
if bucket:
bucket.download_file(loaded_checkpoint, "saved_model.pth.tar") # TODO: is this still valid?
model, _ = load_from_checkpoint("saved_model.pth.tar", model)
else:
model, _ = load_from_checkpoint(loaded_checkpoint, model)
# Save tracking TODO put option not just mlflow
if 'tracker_uri' in locals() and 'run_name' in locals():
mode = get_key_def('mode', params, expected_type=str)
task = get_key_def('task_name', params['task'], expected_type=str)
run_name = '{}_{}_{}'.format(run_name, mode, task)
# tracking path + parameters logging
set_tracking_uri(tracker_uri)
set_experiment(experiment_name)
start_run(run_name=run_name)
log_params(dict_path(params, 'inference'))
log_params(dict_path(params, 'dataset'))
log_params(dict_path(params, 'model'))
# LOOP THROUGH LIST OF INPUT IMAGES
for info in tqdm(list_img, desc='Inferring from images', position=0, leave=True):
img_name = Path(info['tif']).name
local_gpkg = Path(info['gpkg']) if 'gpkg' in info.keys() and info['gpkg'] else None
gpkg_name = local_gpkg.stem if local_gpkg else None
if bucket:
local_img = f"Images/{img_name}"
bucket.download_file(info['tif'], local_img)
inference_image = f"Classified_Images/{img_name.split('.')[0]}_inference.tif"
if info['meta']:
if info['meta'] not in bucket_file_cache:
bucket_file_cache.append(info['meta'])
bucket.download_file(info['meta'], info['meta'].split('/')[-1])
info['meta'] = info['meta'].split('/')[-1]
else: # FIXME: else statement should support img['meta'] integration as well.
local_img = Path(info['tif'])
Path.mkdir(working_folder.joinpath(local_img.parent.name), parents=True, exist_ok=True)
inference_image = working_folder.joinpath(local_img.parent.name,
f"{img_name.split('.')[0]}_inference.tif")
temp_file = working_folder.joinpath(local_img.parent.name, f"{img_name.split('.')[0]}.dat")
raster = rasterio.open(local_img, 'r')
logging.info(f'\nReading original image: {raster.name}')
inf_meta = raster.meta
label = None
if local_gpkg:
logging.info(f'\nBurning label as raster: {local_gpkg}')
local_img = clip_raster_with_gpkg(raster, local_gpkg)
raster.close()
raster = rasterio.open(local_img, 'r')
logging.info(f'\nReading clipped image: {raster.name}')
inf_meta = raster.meta
label = vector_to_raster(vector_file=local_gpkg,
input_image=raster,
out_shape=(inf_meta['height'], inf_meta['width']),
attribute_name=info['attribute_name'],
fill=0, # background value in rasterized vector.
target_ids=targ_ids)
if debug:
logging.debug(f'\nUnique values in loaded label as raster: {np.unique(label)}\n'
f'Shape of label as raster: {label.shape}')
pred, gdf = segmentation(param=params,
input_image=raster,
label_arr=label,
num_classes=num_classes_backgr,
gpkg_name=gpkg_name,
model=model,
chunk_size=chunk_size,
device=device,
scale=scale,
BGR_to_RGB=BGR_to_RGB,
tp_mem=temp_file,
debug=debug)
if gdf is not None:
gdf_.append(gdf)
gpkg_name_.append(gpkg_name)
if local_gpkg and 'tracker_uri' in locals():
pixelMetrics = ComputePixelMetrics(label, pred, num_classes_backgr)
log_metrics(pixelMetrics.update(pixelMetrics.iou))
log_metrics(pixelMetrics.update(pixelMetrics.dice))
pred = pred[np.newaxis, :, :].astype(np.uint8)
inf_meta.update({"driver": "GTiff",
"height": pred.shape[1],
"width": pred.shape[2],
"count": pred.shape[0],
"dtype": 'uint8',
"compress": 'lzw'})
logging.info(f'\nSuccessfully inferred on {img_name}\nWriting to file: {inference_image}')
with rasterio.open(inference_image, 'w+', **inf_meta) as dest:
dest.write(pred)
del pred
try:
temp_file.unlink()
except OSError as e:
logging.warning(f'File Error: {temp_file, e.strerror}')
if raster_to_vec:
start_vec = time.time()
inference_vec = working_folder.joinpath(local_img.parent.name,
f"{img_name.split('.')[0]}_inference.gpkg")
ras2vec(inference_image, inference_vec)
end_vec = time.time() - start_vec
logging.info('Vectorization completed in {:.0f}m {:.0f}s'.format(end_vec // 60, end_vec % 60))
if len(gdf_) >= 1:
if not len(gdf_) == len(gpkg_name_):
raise logging.critical(ValueError('\nbenchmarking unable to complete'))
all_gdf = pd.concat(gdf_) # Concatenate all geo data frame into one geo data frame
all_gdf.reset_index(drop=True, inplace=True)
gdf_x = gpd.GeoDataFrame(all_gdf)
bench_gpkg = working_folder / "benchmark.gpkg"
gdf_x.to_file(bench_gpkg, driver="GPKG", index=False)
logging.info(f'\nSuccessfully wrote benchmark geopackage to: {bench_gpkg}')
# log_artifact(working_folder)
def main(params: dict):
"""
Identify the class to which each image belongs.
:param params: (dict) Parameters found in the yaml config file.
"""
# SET BASIC VARIABLES AND PATHS
since = time.time()
task = params['global']['task']
img_dir_or_csv = params['inference']['img_dir_or_csv_file']
chunk_size = get_key_def('chunk_size', params['inference'], 512)
prediction_with_smoothing = get_key_def('smooth_prediction',
params['inference'], False)
overlap = get_key_def('overlap', params['inference'], 2)
num_classes = params['global']['num_classes']
num_classes_corrected = add_background_to_num_class(task, num_classes)
num_bands = params['global']['number_of_bands']
working_folder = Path(
params['inference']['state_dict_path']).parent.joinpath(
f'inference_{num_bands}bands')
num_devices = params['global']['num_gpus'] if params['global'][
'num_gpus'] else 0
Path.mkdir(working_folder, parents=True, exist_ok=True)
print(f'Inferences will be saved to: {working_folder}\n\n')
bucket = None
bucket_file_cache = []
bucket_name = get_key_def('bucket_name', params['global'])
# list of GPU devices that are available and unused. If no GPUs, returns empty list
lst_device_ids = get_device_ids(
num_devices) if torch.cuda.is_available() else []
device = torch.device(f'cuda:{lst_device_ids[0]}' if torch.cuda.
is_available() and lst_device_ids else 'cpu')
if lst_device_ids:
print(
f"Number of cuda devices requested: {num_devices}. Cuda devices available: {lst_device_ids}. Using {lst_device_ids[0]}\n\n"
)
else:
warnings.warn(
f"No Cuda device available. This process will only run on CPU")
# CONFIGURE MODEL
model, state_dict_path, model_name = net(
params, num_channels=num_classes_corrected, inference=True)
try:
model.to(device)
except RuntimeError:
print(f"Unable to use device. Trying device 0")
device = torch.device(f'cuda:0' if torch.cuda.is_available()
and lst_device_ids else 'cpu')
model.to(device)
# mlflow tracking path + parameters logging
set_tracking_uri(
get_key_def('mlflow_uri', params['global'], default="./mlruns"))
set_experiment('gdl-benchmarking/' + working_folder.name)
log_params(params['global'])
log_params(params['inference'])
# CREATE LIST OF INPUT IMAGES FOR INFERENCE
list_img = list_input_images(img_dir_or_csv,
bucket_name,
glob_patterns=["*.tif", "*.TIF"])
if task == 'classification':
# FIXME: why don't we load from checkpoint in classification?
classifier(params, list_img, model, device, working_folder)
elif task == 'segmentation':
# TODO: Add verifications?
if bucket:
bucket.download_file(
state_dict_path,
"saved_model.pth.tar") # TODO: is this still valid?
model, _ = load_from_checkpoint("saved_model.pth.tar", model)
else:
model, _ = load_from_checkpoint(state_dict_path, model)
# LOOP THROUGH LIST OF INPUT IMAGES
with tqdm(list_img, desc='image list', position=0) as _tqdm:
for info in _tqdm:
img_name = Path(info['tif']).name
local_gpkg = info['gpkg']
if local_gpkg:
local_gpkg = Path(local_gpkg)
if bucket:
local_img = f"Images/{img_name}"
bucket.download_file(info['tif'], local_img)
inference_image = f"Classified_Images/{img_name.split('.')[0]}_inference.tif"
if info['meta']:
if info['meta'] not in bucket_file_cache:
bucket_file_cache.append(info['meta'])
bucket.download_file(info['meta'],
info['meta'].split('/')[-1])
info['meta'] = info['meta'].split('/')[-1]
else: # FIXME: else statement should support img['meta'] integration as well.
local_img = Path(info['tif'])
inference_image = working_folder.joinpath(
f"{img_name.split('.')[0]}_inference.tif")
print(inference_image)
assert local_img.is_file(
), f"Could not locate raster file at {local_img}"
with rasterio.open(local_img, 'r') as raster:
inf_meta = raster.meta
if prediction_with_smoothing:
print('Smoothening Predictions with 2D interpolation')
pred = segmentation_with_smoothing(
raster, local_gpkg, model, chunk_size, overlap,
num_bands, device)
else:
pred = segmentation(raster, local_gpkg, model,
chunk_size, num_bands, device)
if local_gpkg:
assert local_gpkg.is_file(
), f"Could not locate gkpg file at {local_gpkg}"
label = vector_to_raster(
vector_file=local_gpkg,
input_image=raster,
out_shape=pred.shape[:2],
attribute_name=info['attribute_name'],
fill=0) # background value in rasterized vector.
with start_run(run_name=img_name, nested=True):
pixelMetrics = ComputePixelMetrics(
label, pred, num_classes_corrected)
log_metrics(
pixelMetrics.update(pixelMetrics.jaccard))
log_metrics(pixelMetrics.update(pixelMetrics.dice))
log_metrics(
pixelMetrics.update(pixelMetrics.accuracy))
log_metrics(
pixelMetrics.update(pixelMetrics.precision))
log_metrics(
pixelMetrics.update(pixelMetrics.recall))
log_metrics(
pixelMetrics.update(pixelMetrics.matthews))
label_classes = np.unique(label)
assert len(colors) >= len(
label_classes
), f'Not enough colors and class names for number of classes in output'
# FIXME: color mapping scheme is hardcoded for now because of memory constraint; To be fixed.
label_rgb = ind2rgb(label, colors)
pred_rgb = ind2rgb(pred, colors)
Image.fromarray((label_rgb).astype(np.uint8),
mode='RGB').save(
os.path.join(
working_folder, 'label_rgb_' +
inference_image.stem + '.png'))
Image.fromarray((pred_rgb).astype(np.uint8),
mode='RGB').save(
os.path.join(
working_folder, 'pred_rgb_' +
inference_image.stem + '.png'))
del label_rgb, pred_rgb
pred = pred[np.newaxis, :, :]
inf_meta.update({
"driver": "GTiff",
"height": pred.shape[1],
"width": pred.shape[2],
"count": pred.shape[0],
"dtype": 'uint8'
})
with rasterio.open(inference_image, 'w+',
**inf_meta) as dest:
dest.write(pred)
log_artifact(working_folder)
time_elapsed = time.time() - since
print('Inference and Benchmarking completed in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
def image_reader_as_array(input_image,
aux_vector_file=None,
aux_vector_attrib=None,
aux_vector_ids=None,
aux_vector_dist_maps=False,
aux_vector_dist_log=True,
aux_vector_scale=None,
clip_gpkg=None,
debug=False):
"""Read an image from a file and return a 3d array (h,w,c)
Args:
input_image: Rasterio file handle holding the (already opened) input raster
scale: optional scaling factor for the raw data
aux_vector_file: optional vector file from which to extract auxiliary shapes
aux_vector_attrib: optional vector file attribute name to parse in order to fetch ids
aux_vector_ids: optional vector ids to target in the vector file above
aux_vector_dist_maps: flag indicating whether aux vector bands should be distance maps or binary maps
aux_vector_dist_log: flag indicating whether log distances should be used in distance maps or not
aux_vector_scale: optional floating point scale factor to multiply to rasterized vector maps
clip_gpkg: optional path to gpkg used to clip input_image
debug: if True, output raster as given by clip_raster_with_gpkg function is saved to disk
Return:
numpy array of the image (possibly concatenated with auxiliary vector channels)
"""
if clip_gpkg:
np_array, input_image = clip_raster_with_gpkg(input_image,
clip_gpkg,
debug=debug)
np_array = np.transpose(np_array, (1, 2, 0)) # send channels last
else:
np_array = np.empty(
[input_image.height, input_image.width, input_image.count],
dtype=np.uint16)
for i in tqdm(
range(input_image.count),
position=1,
leave=False,
desc=f'Reading image bands: {Path(input_image.files[0]).stem}'
):
np_array[:, :, i] = input_image.read(
i + 1) # Bands starts at 1 in rasterio not 0
assert np_array.dtype in ['uint8', 'uint16'], f"Invalid datatype {np_array.dtype}. " \
f"Only uint8 and uint16 are supported in current version"
dataset_nodata = None
if input_image.nodata is not None:
# See: https://rasterio.readthedocs.io/en/latest/topics/masks.html#dataset-masks
dataset_nodata = np_array == input_image.nodata
# Keep only nodata when present across all bands
dataset_nodata = np.all(dataset_nodata, axis=2)
# if requested, load vectors from external file, rasterize, and append distance maps to array
if aux_vector_file is not None:
vec_tensor = vector_to_raster(vector_file=aux_vector_file,
input_image=input_image,
attribute_name=aux_vector_attrib,
fill=0,
target_ids=aux_vector_ids,
merge_all=False)
if aux_vector_dist_maps:
import cv2 as cv # opencv becomes a project dependency only if we need to compute distance maps here
vec_tensor = vec_tensor.astype(np.float32)
for vec_band_idx in range(vec_tensor.shape[2]):
mask = vec_tensor[:, :, vec_band_idx]
kernel = np.ones(3, 3)
# mask = cv.dilate(mask, kernel) # make points and linestring easier to work with
mask = morphology.binary_dilation(
mask,
kernel) # make points and linestring easier to work with
# display_resize = cv.resize(np.where(mask, np.uint8(0), np.uint8(255)), (1000, 1000))
# cv.imshow("mask", display_resize)
dmap = cv.distanceTransform(
np.where(mask, np.uint8(0), np.uint8(255)), cv.DIST_L2,
cv.DIST_MASK_PRECISE)
if aux_vector_dist_log:
dmap = np.log(dmap + 1)
# display_resize = cv.resize(cv.normalize(dmap, None, 0, 1, cv.NORM_MINMAX, dtype=cv.CV_32F), (1000, 1000))
# cv.imshow("dmap1", display_resize)
dmap_inv = cv.distanceTransform(
np.where(mask, np.uint8(255), np.uint8(0)), cv.DIST_L2,
cv.DIST_MASK_PRECISE)
if aux_vector_dist_log:
dmap_inv = np.log(dmap_inv + 1)
# display_resize = cv.resize(cv.normalize(dmap_inv, None, 0, 1, cv.NORM_MINMAX, dtype=cv.CV_32F), (1000, 1000))
# cv.imshow("dmap2", display_resize)
vec_tensor[:, :,
vec_band_idx] = np.where(mask, -dmap_inv, dmap)
# display = cv.normalize(vec_tensor[:, :, vec_band_idx], None, 0, 1, cv.NORM_MINMAX, dtype=cv.CV_32F)
# display_resize = cv.resize(display, (1000, 1000))
# cv.imshow("distmap", display_resize)
# cv.waitKey(0)
if aux_vector_scale:
for vec_band_idx in vec_tensor.shape[2]:
vec_tensor[:, :, vec_band_idx] *= aux_vector_scale
np_array = np.concatenate([np_array, vec_tensor], axis=2)
return np_array, input_image, dataset_nodata
def main(cfg: DictConfig) -> None:
"""
Function that create training, validation and testing datasets preparation.
1. Read csv file and validate existence of all input files and GeoPackages.
2. Do the following verifications:
1. Assert number of bands found in raster is equal to desired number
of bands.
2. Check that `num_classes` is equal to number of classes detected in
the specified attribute for each GeoPackage.
Warning: this validation will not succeed if a Geopackage
contains only a subset of `num_classes` (e.g. 3 of 4).
3. Assert Coordinate reference system between raster and gpkg match.
3. Read csv file and for each line in the file, do the following:
1. Read input image as array with utils.readers.image_reader_as_array().
- If gpkg's extent is smaller than raster's extent,
raster is clipped to gpkg's extent.
- If gpkg's extent is bigger than raster's extent,
gpkg is clipped to raster's extent.
2. Convert GeoPackage vector information into the "label" raster with
utils.utils.vector_to_raster(). The pixel value is determined by the
attribute in the csv file.
3. Create a new raster called "label" with the same properties as the
input image.
4. Read metadata and add to input as new bands (*more details to come*).
5. Crop the arrays in smaller samples of the size `samples_size` of
`your_conf.yaml`. Visual representation of this is provided at
https://medium.com/the-downlinq/broad-area-satellite-imagery-semantic-segmentation-basiss-4a7ea2c8466f
6. Write samples from input image and label into the "val", "trn" or
"tst" hdf5 file, depending on the value contained in the csv file.
Refer to samples_preparation().
-------
:param cfg: (dict) Parameters found in the yaml config file.
"""
try:
import boto3
except ModuleNotFoundError:
logging.warning(
"\nThe boto3 library couldn't be imported. Ignore if not using AWS s3 buckets",
ImportWarning)
pass
# PARAMETERS
num_classes = len(cfg.dataset.classes_dict.keys())
num_bands = len(cfg.dataset.modalities)
modalities = read_modalities(
cfg.dataset.modalities
) # TODO add the Victor module to manage the modalities
debug = cfg.debug
task = cfg.task.task_name
# RAW DATA PARAMETERS
# Data folder
try:
# check if the folder exist
my_data_path = Path(cfg.dataset.raw_data_dir).resolve(strict=True)
logging.info("\nImage directory used '{}'".format(my_data_path))
data_path = Path(my_data_path)
except FileNotFoundError:
raise logging.critical(
"\nImage directory '{}' doesn't exist, please change the path".
format(cfg.dataset.raw_data_dir))
# CSV file
try:
my_csv_path = Path(cfg.dataset.raw_data_csv).resolve(strict=True)
# path.exists(cfg.dataset.raw_data_csv)
logging.info("\nImage csv: '{}'".format(my_csv_path))
csv_file = my_csv_path
except FileNotFoundError:
raise logging.critical(
"\nImage csv '{}' doesn't exist, please change the path".format(
cfg.dataset.raw_data_csv))
# HDF5 data
try:
my_hdf5_path = Path(str(
cfg.dataset.sample_data_dir)).resolve(strict=True)
logging.info("\nThe HDF5 directory used '{}'".format(my_hdf5_path))
Path.mkdir(Path(my_hdf5_path), exist_ok=True, parents=True)
except FileNotFoundError:
logging.info(
"\nThe HDF5 directory '{}' doesn't exist, please change the path.".
format(cfg.dataset.sample_data_dir) +
"\nFor now the HDF5 directory use will be change for '{}'".format(
data_path))
cfg.general.sample_data_dir = str(data_path)
# SAMPLE PARAMETERS
samples_size = get_key_def('input_dim',
cfg['dataset'],
default=256,
expected_type=int)
overlap = get_key_def('overlap', cfg['dataset'], default=0)
min_annot_perc = get_key_def('min_annot_perc', cfg['dataset'], default=0)
val_percent = get_key_def('train_val_percent', cfg['dataset'],
default=0.3)['val'] * 100
samples_folder_name = f'samples{samples_size}_overlap{overlap}_min-annot{min_annot_perc}' \
f'_{num_bands}bands_{cfg.general.project_name}'
samples_dir = data_path.joinpath(samples_folder_name)
if samples_dir.is_dir():
if debug:
# Move existing data folder with a random suffix.
last_mod_time_suffix = datetime.fromtimestamp(
samples_dir.stat().st_mtime).strftime('%Y%m%d-%H%M%S')
shutil.move(
samples_dir,
data_path.joinpath(
f'{str(samples_dir)}_{last_mod_time_suffix}'))
else:
logging.critical(
f'Data path exists: {samples_dir}. Remove it or use a different experiment_name.'
)
raise FileExistsError()
Path.mkdir(samples_dir, exist_ok=False
) # TODO: what if we want to append samples to existing hdf5?
# LOGGING PARAMETERS TODO see logging yaml
experiment_name = cfg.general.project_name
# mlflow_uri = get_key_def('mlflow_uri', params['global'], default="./mlruns")
# OTHER PARAMETERS
metadata = None
meta_map = {} # TODO get_key_def('meta_map', params['global'], default={})
# TODO class_prop get_key_def('class_proportion', params['sample']['sampling_method'], None, expected_type=dict)
class_prop = None
mask_reference = False # TODO get_key_def('mask_reference', params['sample'], default=False, expected_type=bool)
# set dontcare (aka ignore_index) value
dontcare = cfg.dataset.ignore_index if cfg.dataset.ignore_index is not None else -1
if dontcare == 0:
logging.warning(
"\nThe 'dontcare' value (or 'ignore_index') used in the loss function cannot be zero."
" All valid class indices should be consecutive, and start at 0. The 'dontcare' value"
" will be remapped to -1 while loading the dataset, and inside the config from now on."
)
dontcare = -1
# Assert that all items in target_ids are integers (ex.: single-class samples from multi-class label)
targ_ids = None # TODO get_key_def('target_ids', params['sample'], None, expected_type=List)
if targ_ids is list:
for item in targ_ids:
if not isinstance(item, int):
raise logging.critical(
ValueError(
f'\nTarget id "{item}" in target_ids is {type(item)}, expected int.'
))
# OPTIONAL
use_stratification = cfg.dataset.use_stratification if cfg.dataset.use_stratification is not None else False
if use_stratification:
stratd = {
'trn': {
'total_pixels': 0,
'total_counts': {},
'total_props': {}
},
'val': {
'total_pixels': 0,
'total_counts': {},
'total_props': {}
},
'strat_factor': cfg['dataset']['use_stratification']
}
else:
stratd = None
# ADD GIT HASH FROM CURRENT COMMIT TO PARAMETERS (if available and parameters will be saved to hdf5s).
with open_dict(cfg):
cfg.general.git_hash = get_git_hash()
# AWS TODO
bucket_name = cfg.AWS.bucket_name
if bucket_name:
final_samples_folder = None
bucket_name = cfg.AWS.bucket_name
bucket_file_cache = []
s3 = boto3.resource('s3')
bucket = s3.Bucket(bucket_name)
bucket.download_file(csv_file, 'samples_prep.csv')
list_data_prep = read_csv('samples_prep.csv', data_path)
else:
list_data_prep = read_csv(csv_file, data_path)
# IF DEBUG IS ACTIVATE
if debug:
logging.warning(
f'\nDebug mode activated. Some debug features may mobilize extra disk space and cause delays in execution.'
)
# VALIDATION: (1) Assert num_classes parameters == num actual classes in gpkg and (2) check CRS match (tif and gpkg)
valid_gpkg_set = set()
for info in tqdm(list_data_prep, position=0):
validate_raster(info['tif'], num_bands, meta_map)
if info['gpkg'] not in valid_gpkg_set:
gpkg_classes = validate_num_classes(
info['gpkg'],
num_classes,
info['attribute_name'],
dontcare,
target_ids=targ_ids,
)
assert_crs_match(info['tif'], info['gpkg'])
valid_gpkg_set.add(info['gpkg'])
if debug:
# VALIDATION (debug only): Checking validity of features in vector files
for info in tqdm(
list_data_prep,
position=0,
desc=f"Checking validity of features in vector files"):
# TODO: make unit to test this with invalid features.
invalid_features = validate_features_from_gpkg(
info['gpkg'], info['attribute_name'])
if invalid_features:
logging.critical(
f"{info['gpkg']}: Invalid geometry object(s) '{invalid_features}'"
)
number_samples = {'trn': 0, 'val': 0, 'tst': 0}
number_classes = 0
# with open_dict(cfg):
# print(cfg)
trn_hdf5, val_hdf5, tst_hdf5 = create_files_and_datasets(
samples_size=samples_size,
number_of_bands=num_bands,
meta_map=meta_map,
samples_folder=samples_dir,
cfg=cfg)
# creates pixel_classes dict and keys
pixel_classes = {key: 0 for key in gpkg_classes}
background_val = 0
pixel_classes[background_val] = 0
class_prop = validate_class_prop_dict(pixel_classes, class_prop)
pixel_classes[dontcare] = 0
# For each row in csv: (1) burn vector file to raster, (2) read input raster image, (3) prepare samples
logging.info(
f"\nPreparing samples \n Samples_size: {samples_size} \n Overlap: {overlap} "
f"\n Validation set: {val_percent} % of created training samples")
for info in tqdm(list_data_prep, position=0, leave=False):
try:
if bucket_name:
bucket.download_file(info['tif'],
"Images/" + info['tif'].split('/')[-1])
info['tif'] = "Images/" + info['tif'].split('/')[-1]
if info['gpkg'] not in bucket_file_cache:
bucket_file_cache.append(info['gpkg'])
bucket.download_file(info['gpkg'],
info['gpkg'].split('/')[-1])
info['gpkg'] = info['gpkg'].split('/')[-1]
if info['meta']:
if info['meta'] not in bucket_file_cache:
bucket_file_cache.append(info['meta'])
bucket.download_file(info['meta'],
info['meta'].split('/')[-1])
info['meta'] = info['meta'].split('/')[-1]
logging.info(f"\nReading as array: {info['tif']}")
with rasterio.open(info['tif'], 'r') as raster:
# 1. Read the input raster image
np_input_image, raster, dataset_nodata = image_reader_as_array(
input_image=raster,
clip_gpkg=info['gpkg'],
aux_vector_file=get_key_def('aux_vector_file',
cfg['dataset'], None),
aux_vector_attrib=get_key_def('aux_vector_attrib',
cfg['dataset'], None),
aux_vector_ids=get_key_def('aux_vector_ids',
cfg['dataset'], None),
aux_vector_dist_maps=get_key_def('aux_vector_dist_maps',
cfg['dataset'], True),
aux_vector_dist_log=get_key_def('aux_vector_dist_log',
cfg['dataset'], True),
aux_vector_scale=get_key_def('aux_vector_scale',
cfg['dataset'], None))
# 2. Burn vector file in a raster file
logging.info(
f"\nRasterizing vector file (attribute: {info['attribute_name']}): {info['gpkg']}"
)
np_label_raster = vector_to_raster(
vector_file=info['gpkg'],
input_image=raster,
out_shape=np_input_image.shape[:2],
attribute_name=info['attribute_name'],
fill=background_val,
target_ids=targ_ids
) # background value in rasterized vector.
if dataset_nodata is not None:
# 3. Set ignore_index value in label array where nodata in raster (only if nodata across all bands)
np_label_raster[dataset_nodata] = dontcare
if debug:
out_meta = raster.meta.copy()
np_image_debug = np_input_image.transpose(2, 0, 1).astype(
out_meta['dtype'])
out_meta.update({
"driver": "GTiff",
"height": np_image_debug.shape[1],
"width": np_image_debug.shape[2]
})
out_tif = samples_dir / f"{Path(info['tif']).stem}_clipped.tif"
logging.debug(f"Writing clipped raster to {out_tif}")
with rasterio.open(out_tif, "w", **out_meta) as dest:
dest.write(np_image_debug)
out_meta = raster.meta.copy()
np_label_debug = np.expand_dims(
np_label_raster,
axis=2).transpose(2, 0, 1).astype(out_meta['dtype'])
out_meta.update({
"driver": "GTiff",
"height": np_label_debug.shape[1],
"width": np_label_debug.shape[2],
'count': 1
})
out_tif = samples_dir / f"{Path(info['gpkg']).stem}_clipped.tif"
logging.debug(f"\nWriting final rasterized gpkg to {out_tif}")
with rasterio.open(out_tif, "w", **out_meta) as dest:
dest.write(np_label_debug)
# Mask the zeros from input image into label raster.
if mask_reference:
np_label_raster = mask_image(np_input_image, np_label_raster)
if info['dataset'] == 'trn':
out_file = trn_hdf5
elif info['dataset'] == 'tst':
out_file = tst_hdf5
else:
raise logging.critical(
ValueError(
f"\nDataset value must be trn or tst. Provided value is {info['dataset']}"
))
val_file = val_hdf5
metadata = add_metadata_from_raster_to_sample(
sat_img_arr=np_input_image,
raster_handle=raster,
meta_map=meta_map,
raster_info=info)
# Save label's per class pixel count to image metadata
metadata['source_label_bincount'] = {
class_num: count
for class_num, count in enumerate(
np.bincount(np_label_raster.clip(min=0).flatten()))
if count > 0
} # TODO: add this to add_metadata_from[...] function?
np_label_raster = np.reshape(
np_label_raster,
(np_label_raster.shape[0], np_label_raster.shape[1], 1))
# 3. Prepare samples!
number_samples, number_classes = samples_preparation(
in_img_array=np_input_image,
label_array=np_label_raster,
sample_size=samples_size,
overlap=overlap,
samples_count=number_samples,
num_classes=number_classes,
samples_file=out_file,
val_percent=val_percent,
val_sample_file=val_file,
dataset=info['dataset'],
pixel_classes=pixel_classes,
dontcare=dontcare,
image_metadata=metadata,
min_annot_perc=min_annot_perc,
class_prop=class_prop,
stratd=stratd)
# logging.info(f'\nNumber of samples={number_samples}')
out_file.flush()
except OSError:
logging.exception(
f'\nAn error occurred while preparing samples with "{Path(info["tif"]).stem}" (tiff) and '
f'{Path(info["gpkg"]).stem} (gpkg).')
continue
trn_hdf5.close()
val_hdf5.close()
tst_hdf5.close()
pixel_total = 0
# adds up the number of pixels for each class in pixel_classes dict
for i in pixel_classes:
pixel_total += pixel_classes[i]
# calculate the proportion of pixels of each class for the samples created
pixel_classes_dict = {}
for i in pixel_classes:
# prop = round((pixel_classes[i] / pixel_total) * 100, 1) if pixel_total > 0 else 0
pixel_classes_dict[i] = round((pixel_classes[i] / pixel_total) *
100, 1) if pixel_total > 0 else 0
# prints the proportion of pixels of each class for the samples created
msg_pixel_classes = "\n".join("Pixels from class {}: {}%".format(k, v)
for k, v in pixel_classes_dict.items())
logging.info("\n" + msg_pixel_classes)
logging.info(f"\nNumber of samples created: {number_samples}")
if bucket_name and final_samples_folder: # FIXME: final_samples_folder always None in current implementation
logging.info('\nTransfering Samples to the bucket')
bucket.upload_file(samples_dir + "/trn_samples.hdf5",
final_samples_folder + '/trn_samples.hdf5')
bucket.upload_file(samples_dir + "/val_samples.hdf5",
final_samples_folder + '/val_samples.hdf5')
bucket.upload_file(samples_dir + "/tst_samples.hdf5",
final_samples_folder + '/tst_samples.hdf5')