Cleanup more cases of potential circular impots

thelper/data/geo/gdl.py

@@ -9,23 +9,26 @@
 """
 
 import collections
+import logging
 import os
 
 import h5py
 import numpy as np
 
-import thelper.data
 import thelper.nn.coordconv
+from thelper.data.parsers import SegmentationDataset
 
+logger = logging.getLogger(__name__)
 
-class SegmentationDataset(thelper.data.parsers.SegmentationDataset):
+
+class SegmentationDataset(SegmentationDataset):
     """Semantic segmentation dataset interface for GDL-based HDF5 parsing."""
 
     def __init__(self, class_names, work_folder, dataset_type, max_sample_count=None,
                  dontcare=None, transforms=None):
         self.dontcare = dontcare
         if isinstance(dontcare, (tuple, list)) and len(dontcare) == 2:
-            thelper.data.logger.warning(f"will remap dontcare index from {dontcare[0]} to {dontcare[1]}")
+            logger.warning(f"will remap dontcare index from {dontcare[0]} to {dontcare[1]}")
             dontcare = dontcare[1]
         assert dontcare is None or isinstance(dontcare, int), "unexpected dontcare type"
         super().__init__(class_names=class_names, input_key="sat_img", label_map_key="map_img",

thelper/data/geo/ogc.py

@@ -8,9 +8,10 @@
 import numpy as np
 import tqdm
 
-import thelper.data
-import thelper.train.utils
+import thelper.tasks
+import thelper.utils
 from thelper.data.geo.parsers import TileDataset, VectorCropDataset
+from thelper.train.utils import DetectLogger
 
 logger = logging.getLogger(__name__)
 
@@ -354,7 +355,7 @@ def __getitem__(self, idx):
         return sample
 
 
-class TB15D104DetectLogger(thelper.train.utils.DetectLogger):
+class TB15D104DetectLogger(DetectLogger):
 
     def __init__(self, conf_threshold=0.5):
         super().__init__(conf_threshold=conf_threshold, target_name="lake",
@@ -407,10 +408,10 @@ def postproc_features(input_file, bboxes_srs, orig_geoms_path, output_file,
         bboxes_srs = bboxes_srs_obj
     logger.debug("importing lake bboxes geojson...")
     with open(input_file) as bboxes_fd:
-        bboxes_geoms = thelper.data.geo.utils.parse_geojson(json.load(bboxes_fd))
+        bboxes_geoms = geo.utils.parse_geojson(json.load(bboxes_fd))
     logger.debug("importing hydro features geojson...")
     with open(orig_geoms_path) as hydro_fd:
-        hydro_geoms = thelper.data.geo.utils.parse_geojson(json.load(hydro_fd), srs_target=bboxes_srs)
+        hydro_geoms = geo.utils.parse_geojson(json.load(hydro_fd), srs_target=bboxes_srs)
     logger.debug("computing global cascade of lake bboxes...")
     detect_roi = shapely.ops.cascaded_union([bbox["geometry"] for bbox in bboxes_geoms])
     output_features = []

thelper/data/geo/parsers.py

@@ -15,13 +15,14 @@
 import shapely
 import tqdm
 
-import thelper.data
-import thelper.data.geo as geo
+import thelper.tasks
+import thelper.utils
+from thelper.data import Dataset
 
 logger = logging.getLogger(__name__)
 
 
-class VectorCropDataset(thelper.data.Dataset):
+class VectorCropDataset(Dataset):
     """Abstract dataset used to combine geojson vector data and rasters."""
 
     def __init__(self, raster_path, vector_path, px_size=None, skew=None,
@@ -32,6 +33,7 @@ def __init__(self, raster_path, vector_path, px_size=None, skew=None,
                  cleaner=None, cropper=None, force_parse=False,
                  reproj_rasters=False, reproj_all_cpus=True,
                  keep_rasters_open=True, transforms=None):
+        import thelper.data.geo as geo
         # before anything else, create a hash to cache parsed data
         cache_hash = thelper.utils.get_params_hash(
             {k: v for k, v in vars().items() if not k.startswith("_") and k != "self"}) if not force_parse else None
@@ -130,6 +132,7 @@ def _default_feature_cleaner(features, area_min, area_max, target_prop=None):
     def _default_feature_cropper(features, rasters_data, coverage, srs_target, px_size, skew, feature_buffer):
         """Returns the samples for a set of features (may be modified in derived classes)."""
         # note: default behavior = just center on the feature, and pad if required by user
+        import thelper.data.geo as geo
         samples = []
         clean_feats = [f for f in features if f["clean"]]
         srs_target_wkt = srs_target.ExportToWkt()
@@ -165,6 +168,7 @@ def _default_feature_cropper(features, rasters_data, coverage, srs_target, px_si
     @staticmethod
     def _parse_rasters(path, srs, reproj_rasters):
         """Parses rasters (geotiffs) and returns metadata/coverage information."""
+        import thelper.data.geo as geo
         logger.info(f"parsing rasters from path '{path}'...")
         raster_paths = thelper.utils.get_file_paths(path, ".", allow_glob=True)
         rasters_data, coverage = geo.utils.parse_rasters(raster_paths, srs, reproj_rasters)
@@ -186,6 +190,7 @@ def _parse_rasters(path, srs, reproj_rasters):
     @staticmethod
     def _parse_features(path, srs, roi, cache_hash, allow_outlying, clip_outlying, cleaner):
         """Parses vector files (geojsons) and returns geometry information."""
+        import thelper.data.geo as geo
         logger.info(f"parsing vectors from path '{path}'...")
         assert os.path.isfile(path) and path.endswith("geojson"), \
             "vector file must be provided as geojson (shapefile support still incomplete)"
@@ -231,6 +236,7 @@ def _parse_crops(self, cropper, cache_file_path, cache_hash):
     def _process_crop(self, sample):
         """Returns a crop for a specific (internal) set of sampled features."""
         # remember: we assume that all rasters have the same intrinsic settings
+        import thelper.data.geo as geo
         crop_datatype = geo.utils.GDAL2NUMPY_TYPE_CONV[self.rasters_data[0]["data_type"]]
         crop_size = (sample["crop_height"], sample["crop_width"], self.rasters_data[0]["band_count"])
         crop = np.ma.array(np.zeros(crop_size, dtype=crop_datatype), mask=np.ones(crop_size, dtype=np.uint8))
@@ -341,6 +347,7 @@ def _tile_cropper(features, rasters_data, coverage, srs_target, tile_size, tile_
         """Returns the ROI information for a given feature (may be modified in derived classes)."""
         # instead of iterating over features to generate samples, we tile the raster(s)
         # note: the 'coverage' geometry should already be in the target srs
+        import thelper.data.geo as geo
         roi_tl, roi_br = geo.utils.get_feature_bbox(coverage)
         roi_geotransform = (roi_tl[0], px_size[0], 0.0,
                             roi_tl[1], 0.0, px_size[1])