From 6199d1b71364944c87cef014e6e675cc832ce62a Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Fri, 30 Jun 2023 13:46:01 +0000 Subject: [PATCH] changes to to_traintest_folders function --- detectree2/preprocessing/tiling.py | 14 +++++++++++--- docs/source/tutorial.rst | 19 +++++++++++++------ report/figures/Danum_example_data.png | Bin 0 -> 2788915 bytes 3 files changed, 24 insertions(+), 9 deletions(-) create mode 100644 report/figures/Danum_example_data.png diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index ae4beb6e..55924766 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -443,14 +443,18 @@ def to_traintest_folders(tiles_folder: str = "./", out_folder: str = "./data/", test_frac: float = 0.2, folds: int = 1, + strict: bool = False, seed: int = None) -> None: - """Send tiles to training (+validation) and test dir and automatically make sure no overlap. + """Send tiles to training (+validation) and test dir + + With "strict" it is possible to automatically ensure no overlap between train/val and test tiles. Args: tiles_folder: folder with tiles out_folder: folder to save train and test folders test_frac: fraction of tiles to be used for testing folds: number of folds to split the data into + strict: if True, training/validation files will be removed if there is any overlap with test files (inc buffer) Returns: None @@ -482,14 +486,18 @@ def to_traintest_folders(tiles_folder: str = "./", for i in range(0, len(file_roots)): # copy to test - if i <= len(file_roots) * test_frac: + if i < len(file_roots) * test_frac: test_boxes.append(image_details(file_roots[num[i]])) shutil.copy((tiles_dir / file_roots[num[i]]).with_suffix( Path(file_roots[num[i]]).suffix + ".geojson"), out_dir / "test") else: # copy to train train_box = image_details(file_roots[num[i]]) - if not is_overlapping_box(test_boxes, train_box): + if strict: # check if there is overlap with test boxes + if not is_overlapping_box(test_boxes, train_box): + shutil.copy((tiles_dir / file_roots[num[i]]).with_suffix( + Path(file_roots[num[i]]).suffix + ".geojson"), out_dir / "train") + else: shutil.copy((tiles_dir / file_roots[num[i]]).with_suffix( Path(file_roots[num[i]]).suffix + ".geojson"), out_dir / "train") diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index 184195b7..c7610818 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -19,9 +19,15 @@ To train a model you will need an orthomosaic (as ``.tif``) and corresponding tree crown polgons that are readable by Geopandas (e.g. ``.gpkg``, ``.shp``). For the best results, manual crowns should be supplied as dense clusters rather than -sparsely scattered across in the landscape +sparsely scattered across in the landscape. See below for an example of the +required input crowns and image. +.. image:: ../../report/figures/Danum_example_data.png + :width: 400 + :alt: Example Danum training data + :align: center +| If you would just like to make predictions on an orthomosaic with a pre-trained model from the ``model_garden``, skip to part 4 (Generating landscape predictions). @@ -119,13 +125,14 @@ Send geojsons to train folder (with sub-folders for k-fold cross validation) and .. code-block:: python data_folder = out_dir # data_folder is the folder where the .png, .tif, .geojson tiles have been stored - to_traintest_folders(data_folder, out_dir, test_frac=0.15, folds=5) + to_traintest_folders(data_folder, out_dir, test_frac=0.15, strict=True, folds=5) .. note:: - The ``to_traintest_folders`` function automatically removes training/validation geojsons that overlap with test - tiles, ensuring strict spatial separation of the test data. However, this can remove a significant proportion of the - data available to train on so if validation accuracy is a sufficient test of model performance ``test_frac`` can be - set to ``0``. Alternatively, just set a ``test_frac`` value that is smaller than you might otherwise have put. + If ``strict=True``, the ``to_traintest_folders`` function will automatically removes training/validation geojsons + that have any overlap with test tiles (including the buffers), ensuring strict spatial separation of the test data. + However, this can remove a significant proportion of the data available to train on so if validation accuracy is a + sufficient test of model performance ``test_frac`` can be set to ``0`` or set ``strict=False`` (which allows for + some overlap in the buffers between test and train/val tiles). 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