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import skimage.io
from functools import partial
from multiprocess import Pool
import itertools
import numpy as np
import rasterio
import rasterio.rio.insp
import fiona
from pyproj import Proj, transform, CRS
import torch_lydorn.torch.utils.data
import os
from tqdm import tqdm
import torch
from frame_field_learning import data_transforms
from lydorn_utils import run_utils
from lydorn_utils import print_utils
from lydorn_utils import python_utils
from lydorn_utils import polygon_utils
from lydorn_utils import ogr2ogr
from lydorn_utils import image_utils
class LuxcartaBuildings(torch_lydorn.torch.utils.data.Dataset):
def __init__(self, root, transform=None, pre_transform=None, fold="train", patch_size=None, patch_stride=None,
pool_size=1):
assert fold in {"train", "test"}, "fold should be either train of test"
self.fold = fold
self.patch_size = patch_size
self.patch_stride = patch_stride
self.pool_size = pool_size
self._processed_filepaths = None
# TODO: implement pool_size option
super(LuxcartaBuildings, self).__init__(root, transform, pre_transform)
@property
def raw_dir(self):
return os.path.join(self.root, 'raw', self.fold)
@property
def processed_dir(self):
return os.path.join(self.root, 'processed', self.fold)
@property
def raw_file_names(self):
return []
@property
def raw_sample_metadata_list(self):
returned_list = []
for dirname in os.listdir(self.raw_dir):
dirpath = os.path.join(self.raw_dir, dirname)
if os.path.isdir(dirpath):
image_filepath_list = python_utils.get_filepaths(dirpath, endswith_str="_crop.tif",
startswith_str="Ortho",
not_endswith_str=".tif_crop.tif")
gt_polygons_filepath_list = python_utils.get_filepaths(dirpath, endswith_str=".shp",
startswith_str="Building")
mask_filepath_list = python_utils.get_filepaths(dirpath, endswith_str=".kml", startswith_str="zone")
if len(image_filepath_list) and len(gt_polygons_filepath_list):
metadata = {
"dirname": dirname,
"image_filepath": image_filepath_list[0],
"gt_polygons_filepath": gt_polygons_filepath_list[0],
}
if len(mask_filepath_list):
metadata["mask_filepath"] = mask_filepath_list[0]
returned_list.append(metadata)
return returned_list
@property
def processed_filepaths(self):
if self._processed_filepaths is None:
self._processed_filepaths = []
for dirname in os.listdir(self.processed_dir):
dirpath = os.path.join(self.processed_dir, dirname)
if os.path.isdir(dirpath):
filepath_list = python_utils.get_filepaths(dirpath, endswith_str=".pt", startswith_str="data.")
self._processed_filepaths.extend(filepath_list)
return self._processed_filepaths
def __len__(self):
return len(self.processed_filepaths)
def _download(self):
pass
def download(self):
pass
def _get_mask_multi_polygon(self, mask_filepath, shp_srs):
# Read mask and convert to shapefile's crs:
mask_shp_filepath = os.path.join(os.path.dirname(mask_filepath), "mask.shp")
# TODO: see what's up with the warnings:
ogr2ogr.main(["", "-f", "ESRI Shapefile", mask_shp_filepath,
mask_filepath]) # Convert .kml into .shp
mask = fiona.open(mask_shp_filepath, "r")
mask_srs = Proj(mask.crs["init"])
mask_multi_polygon = []
for feat in mask:
mask_polygon = []
for point in feat['geometry']['coordinates'][0]:
long, lat = point[:2] # one 2D point of the LinearRing
x, y = transform(mask_srs, shp_srs, long, lat, always_xy=True) # transform the point
mask_polygon.append((x, y))
mask_multi_polygon.append(mask_polygon)
# mask_polygon is now in UTM proj, ready to compare to shapefile proj
mask.close()
return mask_multi_polygon
def _read_annotations(self, annotations_filepath, raster, mask_filepath=None):
shapefile = fiona.open(annotations_filepath, "r")
shp_srs = Proj(shapefile.crs["init"])
raster_srs = Proj(raster.crs)
# --- Read and crop shapefile with mask if specified --- #
if mask_filepath is not None:
mask_multi_polygon = self._get_mask_multi_polygon(mask_filepath, shp_srs)
else:
mask_multi_polygon = None
process_feat_partial = partial(process_feat, shp_srs=shp_srs, raster_srs=raster_srs,
raster_transform=raster.transform, mask_multi_polygon=mask_multi_polygon)
with Pool() as pool:
out_polygons = list(
tqdm(pool.imap(process_feat_partial, shapefile), desc="Process shp feature", total=len(shapefile),
leave=True))
out_polygons = list(itertools.chain.from_iterable(out_polygons))
shapefile.close()
return out_polygons
def process(self, metadata_list):
progress_bar = tqdm(metadata_list, desc="Pre-process")
for metadata in progress_bar:
progress_bar.set_postfix(image=metadata["dirname"], status="Loading image")
# Load image
# image = skimage.io.imread(metadata["image_filepath"])
raster = rasterio.open(metadata["image_filepath"])
# print(raster)
# print(dir(raster))
# print(raster.meta)
# exit()
progress_bar.set_postfix(image=metadata["dirname"], status="Process shapefile")
mask_filepath = metadata["mask_filepath"] if "mask_filepath" in metadata else None
gt_polygons = self._read_annotations(metadata["gt_polygons_filepath"], raster, mask_filepath=mask_filepath)
# Compute image mean and std
b1, b2, b3 = raster.read()
image = np.stack([b1, b2, b3], axis=-1)
progress_bar.set_postfix(image=metadata["dirname"], status="Compute mean and std")
image_float = image / 255
mean = np.mean(image_float.reshape(-1, image_float.shape[-1]), axis=0)
std = np.std(image_float.reshape(-1, image_float.shape[-1]), axis=0)
if self.patch_size is not None:
# Patch the tile
progress_bar.set_postfix(image=metadata["dirname"], status="Patching")
patch_stride = self.patch_stride if self.patch_stride is not None else self.patch_size
patch_boundingboxes = image_utils.compute_patch_boundingboxes(image.shape[0:2],
stride=patch_stride,
patch_res=self.patch_size)
for i, patch_boundingbox in enumerate(tqdm(patch_boundingboxes, desc="Process patches", leave=False)):
patch_gt_polygons = polygon_utils.crop_polygons_to_patch_if_touch(gt_polygons, patch_boundingbox)
if len(patch_gt_polygons) == 0:
# Do not save patches empty of polygons # TODO: keep empty patches?
break
patch_image = image[patch_boundingbox[0]:patch_boundingbox[2],
patch_boundingbox[1]:patch_boundingbox[3], :]
sample = {
"dirname": metadata["dirname"],
"image": patch_image,
"image_mean": mean,
"image_std": std,
"gt_polygons": patch_gt_polygons,
"image_filepath": metadata["image_filepath"],
}
if self.pre_transform:
sample = self.pre_transform(sample)
filepath = os.path.join(self.processed_dir, sample["dirname"], "data.{:06d}.pt".format(i))
os.makedirs(os.path.dirname(filepath), exist_ok=True)
torch.save(sample, filepath)
else:
# Tile is saved as is
sample = {
"dirname": metadata["dirname"],
"image": image,
"image_mean": mean,
"image_std": std,
"gt_polygons": gt_polygons,
"image_filepath": metadata["image_filepath"],
}
if self.pre_transform:
sample = self.pre_transform(sample)
filepath = os.path.join(self.processed_dir, sample["dirname"], "data.{:06d}.pt".format(0))
os.makedirs(os.path.dirname(filepath), exist_ok=True)
torch.save(sample, filepath)
flag_filepath = os.path.join(self.processed_dir, metadata["dirname"], "flag.json")
flag = python_utils.load_json(flag_filepath)
flag["done"] = True
python_utils.save_json(flag_filepath, flag)
raster.close()
def _process(self):
to_process_metadata_list = []
for metadata in self.raw_sample_metadata_list:
flag_filepath = os.path.join(self.processed_dir, metadata["dirname"], "flag.json")
if os.path.exists(flag_filepath):
flag = python_utils.load_json(flag_filepath)
if not flag["done"]:
to_process_metadata_list.append(metadata)
else:
flag = {
"done": False
}
python_utils.save_json(flag_filepath, flag)
to_process_metadata_list.append(metadata)
if len(to_process_metadata_list) == 0:
return
print('Processing...')
torch_lydorn.torch.utils.data.makedirs(self.processed_dir)
self.process(to_process_metadata_list)
path = os.path.join(self.processed_dir, 'pre_transform.pt')
torch.save(torch_lydorn.torch.utils.data.__repr__(self.pre_transform), path)
path = os.path.join(self.processed_dir, 'pre_filter.pt')
torch.save(torch_lydorn.torch.utils.data.__repr__(self.pre_filter), path)
print('Done!')
def get(self, idx):
filepath = self.processed_filepaths[idx]
data = torch.load(filepath)
data["patch_bbox"] = torch.tensor([0, 0, 0, 0]) # TODO: implement in pre-processing
tile_name = os.path.basename(os.path.dirname(filepath))
patch_name = os.path.basename(filepath)
patch_name = patch_name[len("data."):-len(".pt")]
data["name"] = tile_name + "." + patch_name
return data
def process_feat(feat, shp_srs, raster_srs, raster_transform, mask_multi_polygon=None):
out_polygons = []
if feat['geometry']["type"] == "Polygon":
poly = feat['geometry']['coordinates']
polygons = process_polygon_feat(poly, shp_srs, raster_srs, raster_transform, mask_multi_polygon)
out_polygons.extend(polygons)
elif feat['geometry']["type"] == "MultiPolygon":
for poly in feat['geometry']["coordinates"]:
polygons = process_polygon_feat(poly, shp_srs, raster_srs, raster_transform, mask_multi_polygon)
out_polygons.extend(polygons)
return out_polygons
def process_polygon_feat(in_polygon, shp_srs, raster_srs, raster_transform, mask_multi_polygon=None):
out_polygons = []
points = in_polygon[0] # TODO: handle holes
# Intersect with mask_polygon if specified
if mask_multi_polygon is not None:
multi_polygon_simple = polygon_utils.intersect_polygons(points, mask_multi_polygon)
if multi_polygon_simple is None:
return out_polygons
else:
multi_polygon_simple = [points]
for polygon_simple in multi_polygon_simple:
new_poly = []
for point in polygon_simple:
x, y = point[:2] # 740524.429227941 7175355.263524155
x, y = transform(shp_srs, raster_srs, x, y) # transform the point # 740520.728530676 7175320.732711278
j, i = ~raster_transform * (x, y)
new_poly.append((i, j)) # 2962.534577447921 2457.457061359659
out_polygons.append(np.array(new_poly))
return out_polygons
def get_seg_display(seg):
seg_display = np.zeros([seg.shape[0], seg.shape[1], 4], dtype=np.float)
if len(seg.shape) == 2:
seg_display[..., 0] = seg
seg_display[..., 3] = seg
else:
for i in range(seg.shape[-1]):
seg_display[..., i] = seg[..., i]
seg_display[..., 3] = np.clip(np.sum(seg, axis=-1), 0, 1)
return seg_display
def main():
# --- Params --- #
config_name = "config.luxcarta_dataset"
# --- --- #
# Load config
config = run_utils.load_config(config_name)
if config is None:
print_utils.print_error(
"ERROR: cannot continue without a config file. Exiting now...")
exit()
# Find data_dir
data_dir = python_utils.choose_first_existing_path(config["data_dir_candidates"])
if data_dir is None:
print_utils.print_error("ERROR: Data directory not found!")
exit()
else:
print_utils.print_info("Using data from {}".format(data_dir))
root_dir = os.path.join(data_dir, config["dataset_params"]["root_dirname"])
# --- Transforms: --- #
# --- pre-processing transform (done once then saved on disk):
train_pre_transform = data_transforms.get_offline_transform(config,
augmentations=config["data_aug_params"]["enable"])
eval_pre_transform = data_transforms.get_offline_transform(config, augmentations=False)
# --- Online transform done on the host (CPU):
train_online_cpu_transform = data_transforms.get_online_cpu_transform(config,
augmentations=config["data_aug_params"][
"enable"])
eval_online_cpu_transform = data_transforms.get_online_cpu_transform(config, augmentations=False)
# --- Online transform performed on the device (GPU):
train_online_cuda_transform = data_transforms.get_online_cuda_transform(config,
augmentations=config["data_aug_params"][
"enable"])
eval_online_cuda_transform = data_transforms.get_online_cuda_transform(config, augmentations=False)
# --- --- #
data_patch_size = config["dataset_params"]["data_patch_size"] if config["data_aug_params"]["enable"] else config["dataset_params"]["input_patch_size"]
fold = "test"
if fold == "train":
dataset = LuxcartaBuildings(root_dir,
transform=train_online_cpu_transform,
patch_size=data_patch_size,
patch_stride=config["dataset_params"]["input_patch_size"],
pre_transform=data_transforms.get_offline_transform_patch(),
fold="train",
pool_size=config["num_workers"])
elif fold == "test":
dataset = LuxcartaBuildings(root_dir,
transform=train_online_cpu_transform,
pre_transform=data_transforms.get_offline_transform_patch(),
fold="test",
pool_size=config["num_workers"])
print("# --- Sample 0 --- #")
sample = dataset[0]
print(sample["image"].shape)
print(sample["gt_polygons_image"].shape)
print("# --- Samples --- #")
# for data in tqdm(dataset):
# pass
data_loader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=True, num_workers=0)
print("# --- Batches --- #")
for batch in tqdm(data_loader):
print(batch["image"].shape)
print(batch["gt_polygons_image"].shape)
# Save output to visualize
seg = np.array(batch["gt_polygons_image"][0]) / 255 # First batch
seg = np.moveaxis(seg, 0, -1)
seg_display = get_seg_display(seg)
seg_display = (seg_display * 255).astype(np.uint8)
skimage.io.imsave("gt_seg.png", seg_display)
im = np.array(batch["image"][0])
im = np.moveaxis(im, 0, -1)
skimage.io.imsave('im.png', im)
input("Enter to continue...")
if __name__ == '__main__':
main()
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