import os
import time
import re
import hashlib
import numpy as np
from pathlib import Path
from typing import Dict, Any
from processing import defaultOutputFolder
import rasterio
from qgis.PyQt.QtCore import QCoreApplication
from qgis.core import (Qgis,
QgsGeometry,
QgsCoordinateTransform,
QgsProcessingException,
QgsProcessingAlgorithm,
QgsProcessingParameterRasterLayer,
QgsProcessingParameterFolderDestination,
QgsProcessingParameterBand,
QgsProcessingParameterNumber,
QgsProcessingParameterBoolean,
QgsProcessingParameterFile,
QgsProcessingParameterString,
QgsProcessingParameterEnum,
QgsProcessingParameterExtent,
QgsProcessingParameterCrs,
QgsProcessingParameterDefinition,
)
import torch
import torch.nn as nn
from torch import Tensor
import torch.quantization
from torch.utils.data import DataLoader
import torchvision.transforms as T
import kornia.augmentation as K
import timm
from torchgeo.datasets import RasterDataset, BoundingBox,stack_samples
from torchgeo.samplers import GridGeoSampler, Units
from torchgeo.transforms import AugmentationSequential
from .utils.geo import get_mean_sd_by_band
from .utils.geo import merge_tiles
from .utils.torchgeo import NoBordersGridGeoSampler
def get_model_size(model):
torch.save(model.state_dict(), "temp.p")
size = os.path.getsize("temp.p")/1e6
os.remove('temp.p')
return size
class EncoderAlgorithm(QgsProcessingAlgorithm):
"""
"""
FEAT_OPTION= 'FEAT_OPTION'
INPUT = 'INPUT'
CKPT = 'CKPT'
BANDS = 'BANDS'
STRIDE = 'STRIDE'
SIZE = 'SIZE'
EXTENT = 'EXTENT'
QUANT = 'QUANT'
OUTPUT = 'OUTPUT'
RESOLUTION = 'RESOLUTION'
CRS = 'CRS'
CUDA = 'CUDA'
BATCH_SIZE = 'BATCH_SIZE'
CUDA_ID = 'CUDA_ID'
BACKBONE_CHOICE = 'BACKBONE_CHOICE'
BACKBONE_OPT = 'BACKBONE_OPT'
MERGE_METHOD = 'MERGE_METHOD'
WORKERS = 'WORKERS'
PAUSES = 'PAUSES'
def initAlgorithm(self, config=None):
"""
Here we define the inputs and output of the algorithm, along
with some other properties.
"""
cwd = Path(__file__).parent.absolute()
self.addParameter(
QgsProcessingParameterRasterLayer(
name=self.INPUT,
description=self.tr(
'Input raster layer or image file path'),
defaultValue=os.path.join(cwd,'assets','test.tif'),
),
)
self.addParameter(
QgsProcessingParameterBand(
name=self.BANDS,
description=self.tr('Selected Bands (defaults to all bands selected)'),
defaultValue = None,
parentLayerParameterName=self.INPUT,
optional=True,
allowMultiple=True,
)
)
crs_param = QgsProcessingParameterCrs(
name=self.CRS,
description=self.tr('Target CRS (default to original CRS)'),
optional=True,
)
res_param = QgsProcessingParameterNumber(
name=self.RESOLUTION,
description=self.tr(
'Target resolution in meters (default to native resolution)'),
type=QgsProcessingParameterNumber.Double,
optional=True,
minValue=0,
maxValue=100000
)
cuda_id_param = QgsProcessingParameterNumber(
name=self.CUDA_ID,
description=self.tr(
'CUDA Device ID (choose which GPU to use, default to device 0)'),
type=QgsProcessingParameterNumber.Integer,
defaultValue=0,
minValue=0,
maxValue=9
)
nworkers_param = QgsProcessingParameterNumber(
name=self.WORKERS,
description=self.tr(
'Number of CPU workers for dataloader (0 selects all)'),
type=QgsProcessingParameterNumber.Integer,
defaultValue=0,
minValue=0,
maxValue=10
)
pauses_param = QgsProcessingParameterNumber(
name=self.PAUSES,
description=self.tr(
'Schedule pauses between batches to ease CPU usage (in seconds).'),
type=QgsProcessingParameterNumber.Integer,
defaultValue=0,
minValue=0,
maxValue=10000
)
self.addParameter(
QgsProcessingParameterExtent(
name=self.EXTENT,
description=self.tr(
'Processing extent (default to the entire image)'),
optional=True
)
)
self.addParameter(
QgsProcessingParameterNumber(
name=self.SIZE,
description=self.tr(
'Sampling size (the raster will be sampled in a square with a side of that many pixel)'),
type=QgsProcessingParameterNumber.Integer,
defaultValue = 224,
minValue=1,
maxValue=1024
)
)
self.addParameter(
QgsProcessingParameterNumber(
name=self.STRIDE,
description=self.tr(
'Stride (If smaller than the sampling size, tiles will overlap. If larger, it may cause errors.)'),
type=QgsProcessingParameterNumber.Integer,
defaultValue = 224,
minValue=1,
maxValue=1024
)
)
chkpt_param = QgsProcessingParameterFile(
name=self.CKPT,
description=self.tr(
'Pretrained checkpoint'),
# extension='pth',
fileFilter='Checkpoint Files (*.pth *.pkl);; All Files (*.*)',
optional=True,
defaultValue=None
)
self.addParameter(
QgsProcessingParameterFolderDestination(
self.OUTPUT,
self.tr(
"Output directory (choose the location that the image features will be saved)"),
defaultValue=os.path.join(cwd,'features'),
)
)
self.addParameter(
QgsProcessingParameterBoolean(
self.CUDA,
self.tr("Use GPU if CUDA is available."),
defaultValue=True
)
)
self.backbone_opt = [
'ViT base DINO',
'ViT tiny Imagenet (smallest)',
'ViT base MAE',
'SAM',
'--Empty--'
]
self.timm_backbone_opt = [
'vit_base_patch16_224.dino',
'vit_tiny_patch16_224.augreg_in21k',
'vit_base_patch16_224.mae',
'samvit_base_patch16.sa1b',
]
self.addParameter (
QgsProcessingParameterEnum(
name = self.BACKBONE_OPT,
description = self.tr(
"Pre-selected backbones if you don't know what to pick"),
defaultValue = 0,
options = self.backbone_opt,
)
)
self.addParameter (
QgsProcessingParameterString(
name = self.BACKBONE_CHOICE,
description = self.tr(
'Enter a architecture name if you want to test another backbone (see huggingface.co/timm/)'),
defaultValue = None,
optional=True,
)
)
self.addParameter(
QgsProcessingParameterBoolean(
self.FEAT_OPTION,
self.tr("Display features map"),
defaultValue=True
)
)
self.addParameter(
QgsProcessingParameterNumber(
name=self.BATCH_SIZE,
# large images will be sampled into patches in a grid-like fashion
description=self.tr(
'Batch size (take effect if choose to use GPU and CUDA is available)'),
type=QgsProcessingParameterNumber.Integer,
defaultValue=1,
minValue=1,
maxValue=1024
)
)
self.addParameter(
QgsProcessingParameterBoolean(
self.QUANT,
self.tr("Quantization of the model to reduce space"),
defaultValue=True
)
)
self.merge_options = ['first', 'min', 'max','average','sum', 'count', 'last']
merge_param = QgsProcessingParameterEnum(
name=self.MERGE_METHOD,
description=self.tr(
'Merge method at the end of inference.'),
options=self.merge_options,
defaultValue=0,
)
for param in (
crs_param,
res_param,
chkpt_param,
cuda_id_param,
merge_param,
nworkers_param,
pauses_param
):
param.setFlags(
param.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
self.addParameter(param)
@torch.no_grad()
def processAlgorithm(self, parameters, context, feedback):
"""
Here is where the processing itself takes place.
"""
self.process_options(parameters, context, feedback)
RasterDataset.filename_glob = self.rlayer_name
RasterDataset.all_bands = [
self.rlayer.bandName(i_band) for i_band in range(1, self.rlayer.bandCount()+1)
]
# currently only support rgb bands
input_bands = [self.rlayer.bandName(i_band)
for i_band in self.selected_bands]
feedback.pushInfo(f'create dataset')
if self.crs == self.rlayer.crs():
dataset = RasterDataset(
paths=self.rlayer_dir, crs=None, res=self.res, bands=input_bands, cache=False)
else:
dataset = RasterDataset(
paths=self.rlayer_dir, crs=self.crs.toWkt(), res=self.res, bands=input_bands, cache=False)
extent_bbox = BoundingBox(minx=self.extent.xMinimum(), maxx=self.extent.xMaximum(), miny=self.extent.yMinimum(), maxy=self.extent.yMaximum(),
mint=dataset.index.bounds[4], maxt=dataset.index.bounds[5])
feedback.pushInfo(f'create model')
print(f'create model')
model = timm.create_model(
self.backbone_name,
pretrained=True,
in_chans=len(input_bands),
num_classes=0
)
feedback.pushInfo(f'model done')
print(f'model done')
data_config = timm.data.resolve_model_data_config(model)
_, h, w, = data_config['input_size']
if self.quantization:
feedback.pushInfo(f'before quantization : {get_model_size(model)}')
model = torch.quantization.quantize_dynamic(
model, {nn.Linear}, dtype=torch.qint8
)
feedback.pushInfo(f'after quantization : {get_model_size(model)}')
transform = AugmentationSequential(
T.ConvertImageDtype(torch.float32), # change dtype for normalize to be possible
K.Normalize(self.means,self.sds), # normalize occurs only on raster, not mask
K.Resize((h, w)), # resize to 224*224 pixels, regardless of sampling size
data_keys=["image"],
)
dataset.transforms = transform
# sampler = GridGeoSampler(
# dataset,
# size=self.size,
# stride=self.stride,
# roi=extent_bbox,
# units=Units.PIXELS
# ) # Units.CRS or Units.PIXELS
sampler = NoBordersGridGeoSampler(
dataset,
size=self.size,
stride=self.stride,
roi=extent_bbox,
units=Units.PIXELS
) # Units.CRS or Units.PIXELS
if len(sampler) == 0:
self.load_feature = False
feedback.pushWarning(f'\n !!!No available patch sample inside the chosen extent!!! \n')
if torch.cuda.is_available() and self.use_gpu:
if self.cuda_id + 1 > torch.cuda.device_count():
self.cuda_id = torch.cuda.device_count() - 1
cuda_device = f'cuda:{self.cuda_id}'
device = f'cuda:{self.cuda_id}'
else:
self.batch_size = 1
device = 'cpu'
feedback.pushInfo(f'Device id: {device}')
feedback.pushInfo(f'model to dedvice')
model.to(device=device)
feedback.pushInfo(f'Batch size: {self.batch_size}')
dataloader = DataLoader(
dataset,
batch_size=self.batch_size,
sampler=sampler,
collate_fn=stack_samples,
num_workers=self.nworkers,
)
feedback.pushInfo(f'Patch sample num: {len(sampler)}')
feedback.pushInfo(f'Total batch num: {len(dataloader)}')
feedback.pushInfo(f'\n\n{"-"*16}\nBegining inference \n{"-"*16}\n\n')
## compute parameters hash to have a unique identifier for the run
## some parameters do not change the encoding part of the algorithm
keys_to_remove = ['MERGE_METHOD', 'WORKERS', 'PAUSES']
param_encoder = {key: parameters[key] for key in parameters if key not in keys_to_remove}
param_hash = hashlib.md5(str(param_encoder).encode("utf-8")).hexdigest()
output_subdir = os.path.join(self.output_dir,param_hash)
output_subdir = Path(output_subdir)
output_subdir.mkdir(parents=True, exist_ok=True)
self.output_subdir = output_subdir
feedback.pushInfo(f'output_subdir: {output_subdir}')
last_batch_done = self.get_last_batch_done()
if last_batch_done >= 0:
feedback.pushInfo(f"\n\n {'-'*8} \n Resuming at batch number {last_batch_done}\n {'-'*8} \n\n")
bboxes = [] # keep track of bboxes to have coordinates at the end
elapsed_time_list = []
total = 100 / len(dataloader) if len(dataloader) else 0
for current, sample in enumerate(dataloader):
if current <= last_batch_done:
continue
start_time = time.time()
# Stop the algorithm if cancel button has been clicked
if feedback.isCanceled():
self.load_feature = False
feedback.pushWarning(
self.tr("\n !!!Processing is canceled by user!!! \n"))
break
feedback.pushInfo(f'\n{"-"*8}\nBatch no. {current} loaded')
images = sample['image'].to(device)
if len(images.shape) > 4:
images = images.squeeze(1)
feedback.pushInfo(f'Batch shape {images.shape}')
features = model.forward_features(images)
features = features[:,1:,:] # take only patch tokens
if current <= last_batch_done + 1:
n_patches = int(np.sqrt(features.shape[1]))
features = features.view(features.shape[0],n_patches,n_patches,features.shape[-1])
features = features.detach().cpu().numpy()
feedback.pushInfo(f'Features shape {features.shape}')
self.save_features(features,sample['bbox'], current)
feedback.pushInfo(f'Features saved')
bboxes.extend(sample['bbox'])
if self.pauses != 0:
time.sleep(self.pauses)
end_time = time.time()
# get the execution time of encoder, ms
elapsed_time = (end_time - start_time)
elapsed_time_list.append(elapsed_time)
time_spent = sum(elapsed_time_list)
time_remain = (time_spent / (current + 1)) * \
(len(dataloader) - current - 1)
# TODO: show gpu usage info
# if torch.cuda.is_available() and self.use_gpu:
# gpu_mem_used = torch.cuda.max_memory_reserved(self.sam_model.device) / (1024 ** 3)
# # gpu_mem_free = torch.cuda.mem_get_info(self.sam_model.device)[0] / (1024 ** 3)
# gpu_mem_total = torch.cuda.mem_get_info(self.sam_model.device)[1] / (1024 ** 3)
# feedback.pushInfo(
# f'GPU memory usage: {gpu_mem_used:.2f}GB / {gpu_mem_total:.2f}GB')
# feedback.pushInfo(str(torch.cuda.memory_summary(self.sam_model.device)))
feedback.pushInfo(f"Encoder executed with {elapsed_time:.3f}s")
feedback.pushInfo(f"Time spent: {time_spent:.3f}s")
if time_remain <= 60:
feedback.pushInfo(f"Estimated time remaining: {time_remain:.3f}s \n {'-'*8}")
else:
time_remain_m, time_remain_s = divmod(int(time_remain), 60)
time_remain_h, time_remain_m = divmod(time_remain_m, 60)
feedback.pushInfo(f"Estimated time remaining: {time_remain_h:d}h:{time_remain_m:02d}m:{time_remain_s:02d}s \n" )
# Update the progress bar
feedback.setProgress(int((current+1) * total))
all_tiles = [os.path.join(self.output_subdir,f) for f in os.listdir(self.output_subdir) if f.endswith('.tif')]
dst_path = Path(os.path.join(self.output_subdir,'merged.tiff'))
feedback.pushInfo(f"\n\n{'-'*8}\n Merging tiles \n{'-'*8}\n" )
merge_tiles(
tiles = all_tiles,
dst_path = dst_path,
method = self.merge_method,
)
parameters['OUTPUT_RASTER']=dst_path
return {"Output feature path": self.output_subdir, 'Patch samples saved': self.iPatch, 'OUTPUT_RASTER':dst_path}
def get_last_batch_done(self):
## get largest batch_number achieved
## files are saved with the pattern '{batch_number}_{image_id_within_batch}.tif'
# Regular expression pattern to extract numbers
pattern = re.compile(r'^(\d+)_\d+\.tif$')
# Initialize a set to store unique first numbers
batch_numbers = set()
# Iterate over all files in the directory
for filename in os.listdir(self.output_subdir):
# Match the filename pattern
match = pattern.match(filename)
if match:
# Extract the batch number
batch_number = int(match.group(1))
# Add to the set of batch numbers
batch_numbers.add(batch_number)
# Find the maximum value of the batch numbers
if batch_numbers:
return max(batch_numbers)
else:
return -1
def save_features(
self,
feature: np.ndarray,
bboxes: BoundingBox,
nbatch: int,
):
# iterate over batch_size dimension
for idx in range(feature.shape[0]):
_, height, width, channels = feature.shape
bbox = bboxes[idx]
rio_transform = rasterio.transform.from_bounds(bbox.minx, bbox.miny, bbox.maxx, bbox.maxy, width, height) # west, south, east, north, width, height
feature_path = os.path.join(self.output_subdir, f"{nbatch}_{idx}.tif")
with rasterio.open(
feature_path,
mode="w",
driver="GTiff",
height=height,
width=width,
count=channels,
dtype='float32',
crs=self.crs.toWkt(),
transform=rio_transform
) as ds:
ds.write(np.transpose(feature[idx, ...], (2, 0, 1)))
tags = {
"model_type": self.backbone_name,
}
ds.update_tags(**tags)
self.iPatch += 1
return
def process_options(self,parameters, context, feedback):
self.iPatch = 0
self.feature_dir = ""
self.FEAT_OPTION = self.parameterAsBoolean(
parameters, self.FEAT_OPTION, context)
feedback.pushInfo(
f'PARAMETERS :\n{parameters}')
feedback.pushInfo(
f'CONTEXT :\n{context}')
feedback.pushInfo(
f'FEEDBACK :\n{feedback}')
rlayer = self.parameterAsRasterLayer(
parameters, self.INPUT, context)
if rlayer is None:
raise QgsProcessingException(
self.invalidRasterError(parameters, self.INPUT))
self.selected_bands = self.parameterAsInts(
parameters, self.BANDS, context)
if len(self.selected_bands) == 0:
self.selected_bands = list(range(1, rlayer.bandCount()+1))
if max(self.selected_bands) > rlayer.bandCount():
raise QgsProcessingException(
self.tr("The chosen bands exceed the largest band number!")
)
ckpt_path = self.parameterAsFile(
parameters, self.CKPT, context)
## Use the given backbone name is any, use preselected models otherwise.
input_name = self.parameterAsString(
parameters, self.BACKBONE_CHOICE, context)
if input_name:
self.backbone_name = input_name
else:
backbone_idx = self.parameterAsEnum(
parameters, self.BACKBONE_OPT, context)
self.backbone_name = self.timm_backbone_opt[backbone_idx]
feedback.pushInfo(f'self.backbone_name:{self.backbone_name}')
self.stride = self.parameterAsInt(
parameters, self.STRIDE, context)
self.size = self.parameterAsInt(
parameters, self.SIZE, context)
res = self.parameterAsDouble(
parameters, self.RESOLUTION, context)
crs = self.parameterAsCrs(
parameters, self.CRS, context)
extent = self.parameterAsExtent(
parameters, self.EXTENT, context)
self.quantization = self.parameterAsBoolean(
parameters, self.QUANT, context)
self.use_gpu = self.parameterAsBoolean(
parameters, self.CUDA, context)
self.batch_size = self.parameterAsInt(
parameters, self.BATCH_SIZE, context)
self.output_dir = self.parameterAsString(
parameters, self.OUTPUT, context)
self.cuda_id = self.parameterAsInt(
parameters, self.CUDA_ID, context)
self.pauses = self.parameterAsInt(
parameters, self.PAUSES, context)
self.nworkers = self.parameterAsInt(
parameters, self.WORKERS, context)
merge_method_idx = self.parameterAsEnum(
parameters, self.MERGE_METHOD, context)
self.merge_method = self.merge_options[merge_method_idx]
rlayer_data_provider = rlayer.dataProvider()
# handle crs
if crs is None or not crs.isValid():
crs = rlayer.crs()
feedback.pushInfo(
f'Layer CRS unit is {crs.mapUnits()}') # 0 for meters, 6 for degrees, 9 for unknown
feedback.pushInfo(
f'whether the CRS is a geographic CRS (using lat/lon coordinates) {crs.isGeographic()}')
if crs.mapUnits() == Qgis.DistanceUnit.Degrees:
crs = self.estimate_utm_crs(rlayer.extent())
# target crs should use meters as units
if crs.mapUnits() != Qgis.DistanceUnit.Meters:
feedback.pushInfo(
f'Layer CRS unit is {crs.mapUnits()}')
feedback.pushInfo(
f'whether the CRS is a geographic CRS (using lat/lon coordinates) {crs.isGeographic()}')
raise QgsProcessingException(
self.tr("Only support CRS with the units as meters")
)
# 0 for meters, 6 for degrees, 9 for unknown
UNIT_METERS = 0
UNIT_DEGREES = 6
if rlayer.crs().mapUnits() == UNIT_DEGREES: # Qgis.DistanceUnit.Degrees:
layer_units = 'degrees'
else:
layer_units = 'meters'
# if res is not provided, get res info from rlayer
if np.isnan(res) or res == 0:
res = rlayer.rasterUnitsPerPixelX() # rasterUnitsPerPixelY() is negative
target_units = layer_units
else:
# when given res in meters by users, convert crs to utm if the original crs unit is degree
if crs.mapUnits() != UNIT_METERS: # Qgis.DistanceUnit.Meters:
if rlayer.crs().mapUnits() == UNIT_DEGREES: # Qgis.DistanceUnit.Degrees:
# estimate utm crs based on layer extent
crs = self.estimate_utm_crs(rlayer.extent())
else:
raise QgsProcessingException(
f"Resampling of image with the CRS of {crs.authid()} in meters is not supported.")
target_units = 'meters'
# else:
# res = (rlayer_extent.xMaximum() -
# rlayer_extent.xMinimum()) / rlayer.width()
self.res = res
# handle extent
if extent.isNull():
extent = rlayer.extent() # QgsProcessingUtils.combineLayerExtents(layers, crs, context)
extent_crs = rlayer.crs()
else:
if extent.isEmpty():
raise QgsProcessingException(
self.tr("The extent for processing can not be empty!"))
extent_crs = self.parameterAsExtentCrs(
parameters, self.EXTENT, context)
# if extent crs != target crs, convert it to target crs
if extent_crs != crs:
transform = QgsCoordinateTransform(
extent_crs, crs, context.transformContext())
# extent = transform.transformBoundingBox(extent)
# to ensure coverage of the transformed extent
# convert extent to polygon, transform polygon, then get boundingBox of the new polygon
extent_polygon = QgsGeometry.fromRect(extent)
extent_polygon.transform(transform)
extent = extent_polygon.boundingBox()
extent_crs = crs
# check intersects between extent and rlayer_extent
if rlayer.crs() != crs:
transform = QgsCoordinateTransform(
rlayer.crs(), crs, context.transformContext())
rlayer_extent = transform.transformBoundingBox(
rlayer.extent())
else:
rlayer_extent = rlayer.extent()
if not rlayer_extent.intersects(extent):
raise QgsProcessingException(
self.tr("The extent for processing is not intersected with the input image!"))
feedback.pushInfo(f'backbne type : {self.backbone_name}')
img_width_in_extent = round(
(extent.xMaximum() - extent.xMinimum())/self.res)
img_height_in_extent = round(
(extent.yMaximum() - extent.yMinimum())/self.res)
# Send some information to the user
feedback.pushInfo(
f'Layer path: {rlayer_data_provider.dataSourceUri()}')
# feedback.pushInfo(
# f'Layer band scale: {rlayer_data_provider.bandScale(self.selected_bands[0])}')
feedback.pushInfo(f'Layer name: {rlayer.name()}')
if rlayer.crs().authid():
feedback.pushInfo(f'Layer CRS: {rlayer.crs().authid()}')
else:
feedback.pushInfo(
f'Layer CRS in WKT format: {rlayer.crs().toWkt()}')
feedback.pushInfo(
f'Layer pixel size: {rlayer.rasterUnitsPerPixelX()}, {rlayer.rasterUnitsPerPixelY()} {layer_units}')
feedback.pushInfo(f'Bands selected: {self.selected_bands}')
if crs.authid():
feedback.pushInfo(f'Target CRS: {crs.authid()}')
else:
feedback.pushInfo(f'Target CRS in WKT format: {crs.toWkt()}')
# feedback.pushInfo('Band number is {}'.format(rlayer.bandCount()))
# feedback.pushInfo('Band name is {}'.format(rlayer.bandName(1)))
feedback.pushInfo(f'Target resolution: {self.res} {target_units}')
# feedback.pushInfo('Layer display band name is {}'.format(
# rlayer.dataProvider().displayBandName(1)))
feedback.pushInfo(
(f'Processing extent: minx:{extent.xMinimum():.6f}, maxx:{extent.xMaximum():.6f},'
f'miny:{extent.yMinimum():.6f}, maxy:{extent.yMaximum():.6f}'))
feedback.pushInfo(
(f'Processing image size: (width {img_width_in_extent}, '
f'height {img_height_in_extent})'))
# feedback.pushInfo(
# f'SAM Image Size: {self.sam_model.image_encoder.img_size}')
self.rlayer_path = rlayer.dataProvider().dataSourceUri()
self.rlayer_dir = os.path.dirname(self.rlayer_path)
self.rlayer_name = os.path.basename(self.rlayer_path)
# get mean and sd of dataset from raster metadata
means, sds = get_mean_sd_by_band(self.rlayer_path)
# subset with selected_bands
feedback.pushInfo(f'Selected bands: {self.selected_bands}')
self.means = [means[i-1] for i in self.selected_bands]
self.sds = [sds[i-1] for i in self.selected_bands]
feedback.pushInfo(f'Means for normalization: {self.means}')
feedback.pushInfo(f'Std. dev. for normalization: {self.sds}')
## passing parameters to self once everything has been processed
self.extent = extent
self.rlayer = rlayer
self.crs = crs
# used to handle any thread-sensitive cleanup which is required by the algorithm.
def postProcessAlgorithm(self, context, feedback) -> Dict[str, Any]:
return {}
def tr(self, string):
"""
Returns a translatable string with the self.tr() function.
"""
return QCoreApplication.translate('Processing', string)
def createInstance(self):
return EncoderAlgorithm()
def name(self):
"""
Returns the algorithm name, used for identifying the algorithm. This
string should be fixed for the algorithm, and must not be localised.
The name should be unique within each provider. Names should contain
lowercase alphanumeric characters only and no spaces or other
formatting characters.
"""
return 'encoder'
def displayName(self):
"""
Returns the translated algorithm name, which should be used for any
user-visible display of the algorithm name.
"""
return self.tr('Image Encoder')
def group(self):
"""
Returns the name of the group this algorithm belongs to. This string
should be localised.
"""
return self.tr('')
def groupId(self):
"""
Returns the unique ID of the group this algorithm belongs to. This
string should be fixed for the algorithm, and must not be localised.
The group id should be unique within each provider. Group id should
contain lowercase alphanumeric characters only and no spaces or other
formatting characters.
"""
return ''
def shortHelpString(self):
"""
Returns a localised short helper string for the algorithm. This string
should provide a basic description about what the algorithm does and the
parameters and outputs associated with it..
"""
return self.tr("Generate image features using a deep learning backbone.")
def icon(self):
return 'E'