From 1210e482b88c7a4e7dd0f6d88fb311941ede59da Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 9 Jul 2024 16:42:30 +0000 Subject: [PATCH 01/63] parallelise tiling --- detectree2/preprocessing/tiling.py | 468 +++++++++++++---------------- 1 file changed, 204 insertions(+), 264 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 2ceeb8c4..3a93bb20 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -4,6 +4,7 @@ of models and making landscape predictions. """ +import concurrent.futures import json import os import random @@ -47,6 +48,82 @@ def get_features(gdf: gpd.GeoDataFrame): return [json.loads(gdf.to_json())["features"][0]["geometry"]] +def process_tile( + data: DatasetReader, + out_dir: str, + buffer: int, + tile_width: int, + tile_height: int, + dtype_bool: bool, + minx, + miny, + crs, + tilename +) -> None: + """Process a single tile for making predictions. + + Args: + data: Orthomosaic as a rasterio object in a UTM type projection + out_dir: Output directory + buffer: Overlapping buffer of tiles in meters (UTM) + tile_width: Tile width in meters + tile_height: Tile height in meters + dtype_bool: Flag to edit dtype to prevent black tiles + minx: Minimum x coordinate of tile + miny: Minimum y coordinate of tile + crs: Coordinate reference system + tilename: Name of the tile + + Returns: + None + """ + out_path = Path(out_dir) + out_path_root = out_path / f"{tilename}_{minx}_{miny}_{tile_width}_{buffer}_{crs}" + bbox = box(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer) + geo = gpd.GeoDataFrame({"geometry": bbox}, index=[0], crs=data.crs) + coords = get_features(geo) + out_img, out_transform = mask(data, shapes=coords, crop=True) + + out_sumbands = np.sum(out_img, 0) + zero_mask = np.where(out_sumbands == 0, 1, 0) + nan_mask = np.where(out_sumbands == 765, 1, 0) + sumzero = zero_mask.sum() + sumnan = nan_mask.sum() + totalpix = out_img.shape[1] * out_img.shape[2] + + # If the tile is mostly empty or mostly nan, don't save it + if sumzero > 0.25 * totalpix or sumnan > 0.25 * totalpix: + return + + out_meta = data.meta.copy() + out_meta.update({ + "driver": "GTiff", + "height": out_img.shape[1], + "width": out_img.shape[2], + "transform": out_transform, + "nodata": None, + }) + if dtype_bool: + out_meta.update({"dtype": "uint8"}) + + out_tif = out_path_root.with_suffix(out_path_root.suffix + ".tif") + with rasterio.open(out_tif, "w", **out_meta) as dest: + dest.write(out_img) + + clipped = rasterio.open(out_tif) + arr = clipped.read() + r, g, b = arr[0], arr[1], arr[2] + rgb = np.dstack((b, g, r)) + + # Rescale to 0-255 if necessary + if np.max(g) > 255: + rgb_rescaled = 255 * rgb / 65535 + else: + rgb_rescaled = rgb + + cv2.imwrite(str(out_path_root.with_suffix(out_path_root.suffix + ".png").resolve()), rgb_rescaled) + + def tile_data( data: DatasetReader, out_dir: str, @@ -73,117 +150,131 @@ def tile_data( """ out_path = Path(out_dir) os.makedirs(out_path, exist_ok=True) - crs = CRS.from_string(data.crs.wkt) - crs = crs.to_epsg() + crs = CRS.from_string(data.crs.wkt).to_epsg() tilename = Path(data.name).stem + total_tiles = int(((data.bounds[2] - data.bounds[0]) / tile_width) * ((data.bounds[3] - data.bounds[1]) / tile_height)) - total_tiles = int( - ((data.bounds[2] - data.bounds[0]) / tile_width) * ((data.bounds[3] - data.bounds[1]) / tile_height) - ) + tile_args = [ + (data, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename) + for minx in np.arange(data.bounds[0], data.bounds[2] - tile_width, tile_width, int) + for miny in np.arange(data.bounds[1], data.bounds[3] - tile_height, tile_height, int) + ] - tile_count = 0 - print(f"Tiling to {total_tiles} total tiles") - - for minx in np.arange(data.bounds[0], data.bounds[2] - tile_width, - tile_width, int): - for miny in np.arange(data.bounds[1], data.bounds[3] - tile_height, - tile_height, int): - - tile_count += 1 - # Naming conventions - out_path_root = out_path / f"{tilename}_{minx}_{miny}_{tile_width}_{buffer}_{crs}" - # new tiling bbox including the buffer - bbox = box( - minx - buffer, - miny - buffer, - minx + tile_width + buffer, - miny + tile_height + buffer, - ) - # define the bounding box of the tile, excluding the buffer - # (hence selecting just the central part of the tile) - # bbox_central = box(minx, miny, minx + tile_width, miny + tile_height) - - # turn the bounding boxes into geopandas DataFrames - geo = gpd.GeoDataFrame({"geometry": bbox}, index=[0], crs=data.crs) - # geo_central = gpd.GeoDataFrame( - # {"geometry": bbox_central}, index=[0], crs=from_epsg(4326) - # ) # 3182 - # overlapping_crowns = sjoin(crowns, geo_central, how="inner") - - # here we are cropping the tiff to the bounding box of the tile we want - coords = get_features(geo) - # print("Coords:", coords) - - # define the tile as a mask of the whole tiff with just the bounding box - out_img, out_transform = mask(data, shapes=coords, crop=True) - - # Discard scenes with many out-of-range pixels - out_sumbands = np.sum(out_img, 0) - zero_mask = np.where(out_sumbands == 0, 1, 0) - nan_mask = np.where(out_sumbands == 765, 1, 0) - sumzero = zero_mask.sum() - sumnan = nan_mask.sum() - totalpix = out_img.shape[1] * out_img.shape[2] - if sumzero > 0.25 * totalpix: - continue - elif sumnan > 0.25 * totalpix: - continue - - out_meta = data.meta.copy() - out_meta.update({ - "driver": "GTiff", - "height": out_img.shape[1], - "width": out_img.shape[2], - "transform": out_transform, - "nodata": None, - }) - # dtype needs to be unchanged for some data and set to uint8 for others - if dtype_bool: - out_meta.update({"dtype": "uint8"}) - # print("Out Meta:",out_meta) - - # Saving the tile as a new tiff, named by the origin of the tile. - # If tile appears blank in folder can show the image here and may - # need to fix RGB data or the dtype - # show(out_img) - out_tif = out_path_root.with_suffix(out_path_root.suffix + ".tif") - with rasterio.open(out_tif, "w", **out_meta) as dest: - dest.write(out_img) - - # read in the tile we have just saved - clipped = rasterio.open(out_tif) - # read it as an array - # show(clipped) - arr = clipped.read() - - # each band of the tiled tiff is a colour! - r = arr[0] - g = arr[1] - b = arr[2] - - # stack up the bands in an order appropriate for saving with cv2, - # then rescale to the correct 0-255 range for cv2 - - rgb = np.dstack((b, g, r)) # BGR for cv2 - - if np.max(g) > 255: - rgb_rescaled = 255 * rgb / 65535 - else: - rgb_rescaled = rgb # scale to image - # print("rgb rescaled", rgb_rescaled) - - # save this as jpg or png...we are going for png...again, named with the origin of the specific tile - # here as a naughty method - cv2.imwrite( - str(out_path_root.with_suffix(out_path_root.suffix + ".png").resolve()), - rgb_rescaled, - ) - if tile_count % 50 == 0: - print(f"Processed {tile_count} tiles of {total_tiles} tiles") + with concurrent.futures.ThreadPoolExecutor() as executor: + list(executor.map(lambda args: process_tile(*args), tile_args)) print("Tiling complete") +def process_tile_train(data, + out_dir: str, + buffer, + tile_width, + tile_height, + dtype_bool, + minx, + miny, + crs, + tilename, + crowns, + threshold, + nan_threshold +) -> None: + """Process a single tile for training data. + + Args: + data: Orthomosaic as a rasterio object in a UTM type projection + out_dir: Output directory + buffer: Overlapping buffer of tiles in meters (UTM) + tile_width: Tile width in meters + tile_height: Tile height in meters + dtype_bool: Flag to edit dtype to prevent black tiles + minx: Minimum x coordinate of tile + miny: Minimum y coordinate of tile + crs: Coordinate reference system + tilename: Name of the tile + crowns: Crown polygons as a geopandas dataframe + threshold: Min proportion of the tile covered by crowns to be accepted {0,1} + nan_theshold: Max proportion of tile covered by nans + + Returns: + None + """ + + out_path = Path(out_dir) + out_path_root = out_path / f"{tilename}_{minx}_{miny}_{tile_width}_{buffer}_{crs}" + + minx_buffered = minx - buffer + miny_buffered = miny - buffer + maxx_buffered = minx + tile_width + buffer + maxy_buffered = miny + tile_height + buffer + + bbox = box(minx_buffered, miny_buffered, maxx_buffered, maxy_buffered) + geo = gpd.GeoDataFrame({"geometry": bbox}, index=[0], crs=data.crs) + coords = get_features(geo) + + overlapping_crowns = gpd.clip(crowns, geo) + if overlapping_crowns.empty or (overlapping_crowns.dissolve().area[0] / geo.area[0]) < threshold: + return + + out_img, out_transform = mask(data, shapes=coords, crop=True) + out_sumbands = np.sum(out_img, 0) + zero_mask = np.where(out_sumbands == 0, 1, 0) + nan_mask = np.where(out_sumbands == 765, 1, 0) + sumzero = zero_mask.sum() + sumnan = nan_mask.sum() + totalpix = out_img.shape[1] * out_img.shape[2] + # If the tile is mostly empty or mostly nan, don't save it + if sumzero > nan_threshold * totalpix or sumnan > nan_threshold * totalpix: + return + + out_meta = data.meta.copy() + out_meta.update({ + "driver": "GTiff", + "height": out_img.shape[1], + "width": out_img.shape[2], + "transform": out_transform, + "nodata": None, + }) + if dtype_bool: + out_meta.update({"dtype": "uint8"}) + + out_tif = out_path_root.with_suffix(out_path_root.suffix + ".tif") + with rasterio.open(out_tif, "w", **out_meta) as dest: + dest.write(out_img) + + clipped = rasterio.open(out_tif) + arr = clipped.read() + r, g, b = arr[0], arr[1], arr[2] + rgb = np.dstack((b, g, r)) + + # Rescale if necessary + if np.max(g) > 255: + rgb_rescaled = 255 * rgb / 65535 + else: + rgb_rescaled = rgb + + cv2.imwrite(str(out_path_root.with_suffix(out_path_root.suffix + ".png").resolve()), rgb_rescaled) + + moved = overlapping_crowns.translate(-minx + buffer, -miny + buffer) + scalingx = 1 / (data.transform[0]) + scalingy = -1 / (data.transform[4]) + moved_scaled = moved.scale(scalingx, scalingy, origin=(0, 0)) + impath = {"imagePath": out_path_root.with_suffix(out_path_root.suffix + ".png").as_posix()} + + try: + filename = out_path_root.with_suffix(out_path_root.suffix + ".geojson") + moved_scaled = overlapping_crowns.set_geometry(moved_scaled) + moved_scaled.to_file(driver="GeoJSON", filename=filename) + with open(filename, "r") as f: + shp = json.load(f) + shp.update(impath) + with open(filename, "w") as f: + json.dump(shp, f) + except ValueError: + print("Cannot write empty DataFrame to file.") + return + def tile_data_train( # noqa: C901 data: DatasetReader, out_dir: str, @@ -220,167 +311,16 @@ def tile_data_train( # noqa: C901 out_path = Path(out_dir) os.makedirs(out_path, exist_ok=True) tilename = Path(data.name).stem - crs = CRS.from_string(data.crs.wkt) - crs = crs.to_epsg() - # out_img, out_transform = mask(data, shapes=crowns.buffer(buffer), crop=True) - # Should start from data.bounds[0] + buffer, data.bounds[1] + buffer to avoid later complications - for minx in np.arange(ceil(data.bounds[0]) + buffer, data.bounds[2] - tile_width - buffer, tile_width, int): - for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, int): - - out_path_root = out_path / f"{tilename}_{minx}_{miny}_{tile_width}_{buffer}_{crs}" - - # Calculate the buffered tile dimensions - # tile_width_buffered = tile_width + 2 * buffer - # tile_height_buffered = tile_height + 2 * buffer - - # Calculate the bounding box coordinates with buffer - minx_buffered = minx - buffer - miny_buffered = miny - buffer - maxx_buffered = minx + tile_width + buffer - maxy_buffered = miny + tile_height + buffer - - # Create the affine transformation matrix for the tile - # transform = from_bounds(minx_buffered, miny_buffered, maxx_buffered, - # maxy_buffered, tile_width_buffered, tile_height_buffered) - - bbox = box(minx_buffered, miny_buffered, maxx_buffered, maxy_buffered) - geo = gpd.GeoDataFrame({"geometry": bbox}, index=[0], crs=data.crs) - coords = get_features(geo) - - # Skip if insufficient coverage of crowns - good to have early on to save on unnecessary processing - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - # Warning: - # _crs_mismatch_warn - overlapping_crowns = gpd.clip(crowns, geo) - - # Ignore tiles with no crowns - if overlapping_crowns.empty: - continue - - # Discard tiles that do not have a sufficient coverage of training crowns - if (overlapping_crowns.dissolve().area[0] / geo.area[0]) < threshold: - continue - - # define the tile as a mask of the whole tiff with just the bounding box - out_img, out_transform = mask(data, shapes=coords, crop=True) - - # Discard scenes with many out-of-range pixels - out_sumbands = np.sum(out_img, 0) - zero_mask = np.where(out_sumbands == 0, 1, 0) - nan_mask = np.where(out_sumbands == 765, 1, 0) - sumzero = zero_mask.sum() - sumnan = nan_mask.sum() - totalpix = out_img.shape[1] * out_img.shape[2] - if sumzero > nan_threshold * totalpix: # reject tiles with many 0 cells - continue - elif sumnan > nan_threshold * totalpix: # reject tiles with many NaN cells - continue - - out_meta = data.meta.copy() - out_meta.update({ - "driver": "GTiff", - "height": out_img.shape[1], - "width": out_img.shape[2], - "transform": out_transform, - "nodata": None, - }) - # dtype needs to be unchanged for some data and set to uint8 for others to deal with black tiles - if dtype_bool: - out_meta.update({"dtype": "uint8"}) - - # Saving the tile as a new tiff, named by the origin of the tile. If tile appears blank in folder can show - # the image here and may need to fix RGB data or the dtype - out_tif = out_path_root.with_suffix(out_path_root.suffix + ".tif") - with rasterio.open(out_tif, "w", **out_meta) as dest: - dest.write(out_img) - - # read in the tile we have just saved - clipped = rasterio.open(out_tif) - - # read it as an array - arr = clipped.read() - - # each band of the tiled tiff is a colour! - r = arr[0] - g = arr[1] - b = arr[2] - - # stack up the bands in an order appropriate for saving with cv2, then rescale to the correct 0-255 range - # for cv2. BGR ordering is correct for cv2 (and detectron2) - rgb = np.dstack((b, g, r)) - - # Some rasters need to have values rescaled to 0-255 - # TODO: more robust check - if np.max(g) > 255: - rgb_rescaled = 255 * rgb / 65535 - else: - # scale to image - rgb_rescaled = rgb - - # save this as png, named with the origin of the specific tile - # potentially bad practice - cv2.imwrite( - str(out_path_root.with_suffix(out_path_root.suffix + ".png").resolve()), - rgb_rescaled, - ) + crs = CRS.from_string(data.crs.wkt).to_epsg() + + tile_args = [ + (data, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) + for minx in np.arange(ceil(data.bounds[0]) + buffer, data.bounds[2] - tile_width - buffer, tile_width, int) + for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, int) + ] - # select the crowns that intersect the non-buffered central - # section of the tile using the inner join - # TODO: A better solution would be to clip crowns to tile extent - # overlapping_crowns = sjoin(crowns, geo_central, how="inner") - # Maybe left join to keep information of crowns? - - overlapping_crowns = overlapping_crowns.explode(index_parts=True) - - # Translate to 0,0 to overlay on png - moved = overlapping_crowns.translate(-minx + buffer, -miny + buffer) - - # scale to deal with the resolution - scalingx = 1 / (data.transform[0]) - scalingy = -1 / (data.transform[4]) - moved_scaled = moved.scale(scalingx, scalingy, origin=(0, 0)) - - impath = {"imagePath": out_path_root.with_suffix(out_path_root.suffix + ".png").as_posix()} - - # Save as a geojson, a format compatible with detectron2, again named by the origin of the tile. - # If the box selected from the image is outside of the mapped region due to the image being on a slant - # then the shp file will have no info on the crowns and hence will create an empty gpd Dataframe. - # this causes an error so skip creating geojson. The training code will also ignore png so no problem. - try: - filename = out_path_root.with_suffix(out_path_root.suffix + ".geojson") - moved_scaled = overlapping_crowns.set_geometry(moved_scaled) - moved_scaled.to_file( - driver="GeoJSON", - filename=filename, - ) - with open(filename, "r") as f: - shp = json.load(f) - shp.update(impath) - with open(filename, "w") as f: - json.dump(shp, f) - except ValueError: - print("Cannot write empty DataFrame to file.") - continue - # Repeat and want to save crowns before being moved as overlap with lidar data to get the heights - # can try clean up the code here as lots of reprojecting and resaving but just going to get to - # work for now - out_geo_file = out_path_root.parts[-1] + "_geo" - out_path_geo = out_path / Path(out_geo_file) - try: - filename_unmoved = out_path_geo.with_suffix(out_path_geo.suffix + ".geojson") - overlapping_crowns.to_file( - driver="GeoJSON", - filename=filename_unmoved, - ) - with open(filename_unmoved, "r") as f: - shp = json.load(f) - shp.update(impath) - with open(filename_unmoved, "w") as f: - json.dump(shp, f) - except ValueError: - print("Cannot write empty DataFrame to file.") - continue + with concurrent.futures.ThreadPoolExecutor() as executor: + list(executor.map(lambda args: process_tile_train(*args), tile_args)) print("Tiling complete") From 2eda758be860f0eac0c8a74b79a87a19a32bff0b Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Wed, 10 Jul 2024 12:21:16 +0000 Subject: [PATCH 02/63] parallelise tiling --- detectree2/preprocessing/tiling.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 3a93bb20..321f0b09 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -166,12 +166,13 @@ def tile_data( print("Tiling complete") -def process_tile_train(data, +def process_tile_train( + data: DatasetReader, out_dir: str, - buffer, - tile_width, - tile_height, - dtype_bool, + buffer: int, + tile_width: int, + tile_height: int, + dtype_bool: bool, minx, miny, crs, From 7f2a6c7c2d240195f021cfd923b2343cc009e897 Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Wed, 10 Jul 2024 12:23:34 +0000 Subject: [PATCH 03/63] update readme --- README.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/README.md b/README.md index 2f07f770..e10c2e83 100644 --- a/README.md +++ b/README.md @@ -18,10 +18,6 @@ Detectree2是一个基于Mask R-CNN的自动树冠检测与分割的Python包。 | | Code developed by James Ball, Seb Hickman, Thomas Koay, Oscar Jiang, Luran Wang, Panagiotis Ioannou, James Hinton and Matthew Archer in the [Forest Ecology and Conservation Group](https://coomeslab.org/) at the University of Cambridge. The Forest Ecology and Conservation Group is led by Professor David Coomes and is part of the University of Cambridge [Conservation Research Institute](https://www.conservation.cam.ac.uk/). | | :---: | :--- | -

-> [!NOTE] -> To save bandwidth, trained models have been moved to [Zenodo](https://zenodo.org/records/10522461). Download models directly with `wget` or equivalent. - ## Citation From 4e10abe92201bd3d46b4660c14166dc8dc89b8c2 Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 16 Jul 2024 14:54:32 +0000 Subject: [PATCH 04/63] test partial reading of raster --- detectree2/preprocessing/tiling.py | 29 +++++++++++++++++++++++++++-- 1 file changed, 27 insertions(+), 2 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 321f0b09..53af0671 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -6,6 +6,7 @@ import concurrent.futures import json +import logging import os import random import shutil @@ -21,6 +22,7 @@ from rasterio.crs import CRS from rasterio.io import DatasetReader from rasterio.mask import mask +from rasterio.windows import from_bounds from shapely.geometry import box # class img_data(DatasetReader): @@ -82,7 +84,18 @@ def process_tile( bbox = box(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer) geo = gpd.GeoDataFrame({"geometry": bbox}, index=[0], crs=data.crs) coords = get_features(geo) - out_img, out_transform = mask(data, shapes=coords, crop=True) + + # Create a window corresponding to the bounding box + window = from_bounds(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer, data.transform) + + try: + out_img = data.read(window=window) + out_transform = data.window_transform(window) + except RasterioIOError as e: + logger.error(f"RasterioIOError while reading window {window}: {e}") + return + + #out_img, out_transform = mask(data, shapes=coords, crop=True) out_sumbands = np.sum(out_img, 0) zero_mask = np.where(out_sumbands == 0, 1, 0) @@ -218,7 +231,19 @@ def process_tile_train( if overlapping_crowns.empty or (overlapping_crowns.dissolve().area[0] / geo.area[0]) < threshold: return - out_img, out_transform = mask(data, shapes=coords, crop=True) + # Create a window corresponding to the bounding box + window = from_bounds(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer, data.transform) + + try: + out_img = data.read(window=window) + out_transform = data.window_transform(window) + except RasterioIOError as e: + logger.error(f"RasterioIOError while reading window {window}: {e}") + return + + + #out_img, out_transform = mask(data, shapes=coords, crop=True) + out_sumbands = np.sum(out_img, 0) zero_mask = np.where(out_sumbands == 0, 1, 0) nan_mask = np.where(out_sumbands == 765, 1, 0) From 1ded0d856a6c40506e5db9e65d50532708a47e25 Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 16 Jul 2024 15:03:22 +0000 Subject: [PATCH 05/63] test partial reading of raster --- detectree2/preprocessing/tiling.py | 1 + 1 file changed, 1 insertion(+) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 53af0671..7d9a3da5 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -20,6 +20,7 @@ import rasterio from fiona.crs import from_epsg # noqa: F401 from rasterio.crs import CRS +from rasterio.errors import RasterioIOError from rasterio.io import DatasetReader from rasterio.mask import mask from rasterio.windows import from_bounds From 7c421407fca5d1f9e379e6bddd211732e4b8ac4a Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 16 Jul 2024 15:11:42 +0000 Subject: [PATCH 06/63] add logger for tiling --- detectree2/preprocessing/tiling.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 7d9a3da5..eb043066 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -26,6 +26,10 @@ from rasterio.windows import from_bounds from shapely.geometry import box +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + # class img_data(DatasetReader): # """ # Class for image data to be processed for tiling From aa8ab37d622389924394d1f61b0c5007c872fdcc Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 16 Jul 2024 16:23:50 +0000 Subject: [PATCH 07/63] change back to masking --- detectree2/preprocessing/tiling.py | 35 +++++++++++++++--------------- 1 file changed, 17 insertions(+), 18 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index eb043066..94263d80 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -91,16 +91,16 @@ def process_tile( coords = get_features(geo) # Create a window corresponding to the bounding box - window = from_bounds(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer, data.transform) + #window = from_bounds(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer, data.transform) - try: - out_img = data.read(window=window) - out_transform = data.window_transform(window) - except RasterioIOError as e: - logger.error(f"RasterioIOError while reading window {window}: {e}") - return + #try: + # out_img = data.read(window=window) + # out_transform = data.window_transform(window) + #except RasterioIOError as e: + # logger.error(f"RasterioIOError while reading window {window}: {e}") + # return - #out_img, out_transform = mask(data, shapes=coords, crop=True) + out_img, out_transform = mask(data, shapes=coords, crop=True) out_sumbands = np.sum(out_img, 0) zero_mask = np.where(out_sumbands == 0, 1, 0) @@ -237,17 +237,16 @@ def process_tile_train( return # Create a window corresponding to the bounding box - window = from_bounds(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer, data.transform) + #window = from_bounds(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer, data.transform) - try: - out_img = data.read(window=window) - out_transform = data.window_transform(window) - except RasterioIOError as e: - logger.error(f"RasterioIOError while reading window {window}: {e}") - return - - - #out_img, out_transform = mask(data, shapes=coords, crop=True) + #try: + # out_img = data.read(window=window) + # out_transform = data.window_transform(window) + #except RasterioIOError as e: + # logger.error(f"RasterioIOError while reading window {window}: {e}") + # return + + out_img, out_transform = mask(data, shapes=coords, crop=True) out_sumbands = np.sum(out_img, 0) zero_mask = np.where(out_sumbands == 0, 1, 0) From 5320a50c8648adb978cc68edab64173aa11a9b28 Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 16 Jul 2024 16:38:06 +0000 Subject: [PATCH 08/63] change back to masking --- detectree2/preprocessing/tiling.py | 28 +++++++++++++--------------- 1 file changed, 13 insertions(+), 15 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 94263d80..55ce0d9d 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -93,14 +93,13 @@ def process_tile( # Create a window corresponding to the bounding box #window = from_bounds(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer, data.transform) - #try: - # out_img = data.read(window=window) - # out_transform = data.window_transform(window) - #except RasterioIOError as e: - # logger.error(f"RasterioIOError while reading window {window}: {e}") - # return + try: + out_img, out_transform = mask(data, shapes=coords, crop=True) + except RasterioIOError as e: + logger.error(f"RasterioIOError while reading window {window}: {e}") + return - out_img, out_transform = mask(data, shapes=coords, crop=True) + #out_img, out_transform = mask(data, shapes=coords, crop=True) out_sumbands = np.sum(out_img, 0) zero_mask = np.where(out_sumbands == 0, 1, 0) @@ -239,14 +238,13 @@ def process_tile_train( # Create a window corresponding to the bounding box #window = from_bounds(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer, data.transform) - #try: - # out_img = data.read(window=window) - # out_transform = data.window_transform(window) - #except RasterioIOError as e: - # logger.error(f"RasterioIOError while reading window {window}: {e}") - # return - - out_img, out_transform = mask(data, shapes=coords, crop=True) + try: + out_img, out_transform = mask(data, shapes=coords, crop=True) + except RasterioIOError as e: + logger.error(f"RasterioIOError while reading window {window}: {e}") + return + + #out_img, out_transform = mask(data, shapes=coords, crop=True) out_sumbands = np.sum(out_img, 0) zero_mask = np.where(out_sumbands == 0, 1, 0) From 8e5c68ac1665a11f753893502908886f28e76224 Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 16 Jul 2024 16:48:37 +0000 Subject: [PATCH 09/63] change back to masking --- detectree2/preprocessing/tiling.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 55ce0d9d..2a00cd25 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -96,7 +96,7 @@ def process_tile( try: out_img, out_transform = mask(data, shapes=coords, crop=True) except RasterioIOError as e: - logger.error(f"RasterioIOError while reading window {window}: {e}") + logger.error(f"RasterioIOError: {e}") return #out_img, out_transform = mask(data, shapes=coords, crop=True) @@ -241,7 +241,7 @@ def process_tile_train( try: out_img, out_transform = mask(data, shapes=coords, crop=True) except RasterioIOError as e: - logger.error(f"RasterioIOError while reading window {window}: {e}") + logger.error(f"RasterioIOError: {e}") return #out_img, out_transform = mask(data, shapes=coords, crop=True) From 3fd57995c61ac4f68c99fcf9ab6dd3c649a0c847 Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 16 Jul 2024 17:24:27 +0000 Subject: [PATCH 10/63] img_path instead of reader for multithread --- detectree2/preprocessing/tiling.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 2a00cd25..f89a195a 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -96,7 +96,7 @@ def process_tile( try: out_img, out_transform = mask(data, shapes=coords, crop=True) except RasterioIOError as e: - logger.error(f"RasterioIOError: {e}") + logger.error(f"RasterioIOError while applying mask {coords}: {e}") return #out_img, out_transform = mask(data, shapes=coords, crop=True) @@ -184,7 +184,7 @@ def tile_data( def process_tile_train( - data: DatasetReader, + img_path: str, out_dir: str, buffer: int, tile_width: int, @@ -218,6 +218,7 @@ def process_tile_train( Returns: None """ + data = rasterio.open(img_path) out_path = Path(out_dir) out_path_root = out_path / f"{tilename}_{minx}_{miny}_{tile_width}_{buffer}_{crs}" @@ -241,7 +242,7 @@ def process_tile_train( try: out_img, out_transform = mask(data, shapes=coords, crop=True) except RasterioIOError as e: - logger.error(f"RasterioIOError: {e}") + logger.error(f"RasterioIOError while applying mask {coords}: {e}") return #out_img, out_transform = mask(data, shapes=coords, crop=True) @@ -304,7 +305,7 @@ def process_tile_train( return def tile_data_train( # noqa: C901 - data: DatasetReader, + img_path: str, out_dir: str, buffer: int = 30, tile_width: int = 200, @@ -342,7 +343,7 @@ def tile_data_train( # noqa: C901 crs = CRS.from_string(data.crs.wkt).to_epsg() tile_args = [ - (data, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) + (img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) for minx in np.arange(ceil(data.bounds[0]) + buffer, data.bounds[2] - tile_width - buffer, tile_width, int) for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, int) ] From 93f2c1802f213423a2bd3d405dd886b30913efc8 Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 16 Jul 2024 17:27:32 +0000 Subject: [PATCH 11/63] img_path instead of reader for multithread --- detectree2/preprocessing/tiling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index f89a195a..5479570f 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -339,7 +339,7 @@ def tile_data_train( # noqa: C901 # TODO: Tighten up epsg handling out_path = Path(out_dir) os.makedirs(out_path, exist_ok=True) - tilename = Path(data.name).stem + tilename = Path(img_path).stem crs = CRS.from_string(data.crs.wkt).to_epsg() tile_args = [ From 617b733f7d35ac3a8f7e9281897bea000c2c1cbb Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 16 Jul 2024 17:33:11 +0000 Subject: [PATCH 12/63] img_path instead of reader for multithread --- detectree2/preprocessing/tiling.py | 1 + 1 file changed, 1 insertion(+) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 5479570f..271e066c 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -340,6 +340,7 @@ def tile_data_train( # noqa: C901 out_path = Path(out_dir) os.makedirs(out_path, exist_ok=True) tilename = Path(img_path).stem + data = rasterio.open(img_path) crs = CRS.from_string(data.crs.wkt).to_epsg() tile_args = [ From 24b5d74438d9e0ac4675b3c773b05a0a84497ec8 Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 16 Jul 2024 17:52:26 +0000 Subject: [PATCH 13/63] explode reintroduced --- detectree2/preprocessing/tiling.py | 1 + 1 file changed, 1 insertion(+) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 271e066c..35b0f71e 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -285,6 +285,7 @@ def process_tile_train( cv2.imwrite(str(out_path_root.with_suffix(out_path_root.suffix + ".png").resolve()), rgb_rescaled) + overlapping_crowns = overlapping_crowns.explode(index_parts=True) moved = overlapping_crowns.translate(-minx + buffer, -miny + buffer) scalingx = 1 / (data.transform[0]) scalingy = -1 / (data.transform[4]) From bb63e3c04f6c8b2019d3e28cdae0489c1cd6b29e Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Tue, 16 Jul 2024 18:43:19 +0000 Subject: [PATCH 14/63] refactor tile processing --- detectree2/preprocessing/tiling.py | 101 ++++++++--------------------- 1 file changed, 26 insertions(+), 75 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 35b0f71e..c51a1f7d 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -56,7 +56,7 @@ def get_features(gdf: gpd.GeoDataFrame): def process_tile( - data: DatasetReader, + img_path: str, out_dir: str, buffer: int, tile_width: int, @@ -65,8 +65,11 @@ def process_tile( minx, miny, crs, - tilename -) -> None: + tilename, + crowns: gpd.GeoDataFrame = None, + threshold: float = 0, + nan_threshold: float = 0, +): """Process a single tile for making predictions. Args: @@ -84,15 +87,25 @@ def process_tile( Returns: None """ + data = rasterio.open(img_path) + out_path = Path(out_dir) out_path_root = out_path / f"{tilename}_{minx}_{miny}_{tile_width}_{buffer}_{crs}" - bbox = box(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer) + + minx_buffered = minx - buffer + miny_buffered = miny - buffer + maxx_buffered = minx + tile_width + buffer + maxy_buffered = miny + tile_height + buffer + + bbox = box(minx_buffered, miny_buffered, maxx_buffered, maxy_buffered) geo = gpd.GeoDataFrame({"geometry": bbox}, index=[0], crs=data.crs) coords = get_features(geo) - # Create a window corresponding to the bounding box - #window = from_bounds(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer, data.transform) - + if crowns is not None: + overlapping_crowns = gpd.clip(crowns, geo) + if overlapping_crowns.empty or (overlapping_crowns.dissolve().area[0] / geo.area[0]) < threshold: + return + try: out_img, out_transform = mask(data, shapes=coords, crop=True) except RasterioIOError as e: @@ -109,7 +122,7 @@ def process_tile( totalpix = out_img.shape[1] * out_img.shape[2] # If the tile is mostly empty or mostly nan, don't save it - if sumzero > 0.25 * totalpix or sumnan > 0.25 * totalpix: + if sumzero > nan_threshold * totalpix or sumnan > nan_threshold * totalpix: return out_meta = data.meta.copy() @@ -139,6 +152,9 @@ def process_tile( rgb_rescaled = rgb cv2.imwrite(str(out_path_root.with_suffix(out_path_root.suffix + ".png").resolve()), rgb_rescaled) + if overlapping_crowns is not None: + return data, out_path_root, overlapping_crowns, minx, miny, buffer + def tile_data( @@ -194,7 +210,7 @@ def process_tile_train( miny, crs, tilename, - crowns, + crowns: gpd.GeoDataFrame, threshold, nan_threshold ) -> None: @@ -218,72 +234,7 @@ def process_tile_train( Returns: None """ - data = rasterio.open(img_path) - - out_path = Path(out_dir) - out_path_root = out_path / f"{tilename}_{minx}_{miny}_{tile_width}_{buffer}_{crs}" - - minx_buffered = minx - buffer - miny_buffered = miny - buffer - maxx_buffered = minx + tile_width + buffer - maxy_buffered = miny + tile_height + buffer - - bbox = box(minx_buffered, miny_buffered, maxx_buffered, maxy_buffered) - geo = gpd.GeoDataFrame({"geometry": bbox}, index=[0], crs=data.crs) - coords = get_features(geo) - - overlapping_crowns = gpd.clip(crowns, geo) - if overlapping_crowns.empty or (overlapping_crowns.dissolve().area[0] / geo.area[0]) < threshold: - return - - # Create a window corresponding to the bounding box - #window = from_bounds(minx - buffer, miny - buffer, minx + tile_width + buffer, miny + tile_height + buffer, data.transform) - - try: - out_img, out_transform = mask(data, shapes=coords, crop=True) - except RasterioIOError as e: - logger.error(f"RasterioIOError while applying mask {coords}: {e}") - return - - #out_img, out_transform = mask(data, shapes=coords, crop=True) - - out_sumbands = np.sum(out_img, 0) - zero_mask = np.where(out_sumbands == 0, 1, 0) - nan_mask = np.where(out_sumbands == 765, 1, 0) - sumzero = zero_mask.sum() - sumnan = nan_mask.sum() - totalpix = out_img.shape[1] * out_img.shape[2] - # If the tile is mostly empty or mostly nan, don't save it - if sumzero > nan_threshold * totalpix or sumnan > nan_threshold * totalpix: - return - - out_meta = data.meta.copy() - out_meta.update({ - "driver": "GTiff", - "height": out_img.shape[1], - "width": out_img.shape[2], - "transform": out_transform, - "nodata": None, - }) - if dtype_bool: - out_meta.update({"dtype": "uint8"}) - - out_tif = out_path_root.with_suffix(out_path_root.suffix + ".tif") - with rasterio.open(out_tif, "w", **out_meta) as dest: - dest.write(out_img) - - clipped = rasterio.open(out_tif) - arr = clipped.read() - r, g, b = arr[0], arr[1], arr[2] - rgb = np.dstack((b, g, r)) - - # Rescale if necessary - if np.max(g) > 255: - rgb_rescaled = 255 * rgb / 65535 - else: - rgb_rescaled = rgb - - cv2.imwrite(str(out_path_root.with_suffix(out_path_root.suffix + ".png").resolve()), rgb_rescaled) + data, out_path_root, overlapping_crowns, minx, miny, buffer = process_tile(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) overlapping_crowns = overlapping_crowns.explode(index_parts=True) moved = overlapping_crowns.translate(-minx + buffer, -miny + buffer) From b42291649b0766ac4d374480f1e982d223e6498c Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Wed, 17 Jul 2024 10:34:56 +0000 Subject: [PATCH 15/63] refactor tiling --- detectree2/preprocessing/tiling.py | 96 ++++++++++++++++-------------- 1 file changed, 50 insertions(+), 46 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index c51a1f7d..d8940c7a 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -73,7 +73,7 @@ def process_tile( """Process a single tile for making predictions. Args: - data: Orthomosaic as a rasterio object in a UTM type projection + img_path: Path to the orthomosaic out_dir: Output directory buffer: Overlapping buffer of tiles in meters (UTM) tile_width: Tile width in meters @@ -100,60 +100,64 @@ def process_tile( bbox = box(minx_buffered, miny_buffered, maxx_buffered, maxy_buffered) geo = gpd.GeoDataFrame({"geometry": bbox}, index=[0], crs=data.crs) coords = get_features(geo) - + + overlapping_crowns = None if crowns is not None: overlapping_crowns = gpd.clip(crowns, geo) if overlapping_crowns.empty or (overlapping_crowns.dissolve().area[0] / geo.area[0]) < threshold: return try: - out_img, out_transform = mask(data, shapes=coords, crop=True) - except RasterioIOError as e: - logger.error(f"RasterioIOError while applying mask {coords}: {e}") - return - - #out_img, out_transform = mask(data, shapes=coords, crop=True) + with rasterio.open(img_path) as data: + out_img, out_transform = mask(data, shapes=coords, crop=True) + + out_sumbands = np.sum(out_img, axis=0) + zero_mask = np.where(out_sumbands == 0, 1, 0) + nan_mask = np.where(out_sumbands == 765, 1, 0) + sumzero = zero_mask.sum() + sumnan = nan_mask.sum() + totalpix = out_img.shape[1] * out_img.shape[2] + + # If the tile is mostly empty or mostly nan, don't save it + if sumzero > nan_threshold * totalpix or sumnan > nan_threshold * totalpix: + return None + + out_meta = data.meta.copy() + out_meta.update({ + "driver": "GTiff", + "height": out_img.shape[1], + "width": out_img.shape[2], + "transform": out_transform, + "nodata": None, + }) + if dtype_bool: + out_meta.update({"dtype": "uint8"}) + + out_tif = out_path_root.with_suffix(out_path_root.suffix + ".tif") + with rasterio.open(out_tif, "w", **out_meta) as dest: + dest.write(out_img) + + with rasterio.open(out_tif) as clipped: + arr = clipped.read() + r, g, b = arr[0], arr[1], arr[2] + rgb = np.dstack((b, g, r)) + + # Rescale to 0-255 if necessary + if np.max(g) > 255: + rgb_rescaled = 255 * rgb / 65535 + else: + rgb_rescaled = rgb - out_sumbands = np.sum(out_img, 0) - zero_mask = np.where(out_sumbands == 0, 1, 0) - nan_mask = np.where(out_sumbands == 765, 1, 0) - sumzero = zero_mask.sum() - sumnan = nan_mask.sum() - totalpix = out_img.shape[1] * out_img.shape[2] + cv2.imwrite(str(out_path_root.with_suffix(out_path_root.suffix + ".png").resolve()), rgb_rescaled) - # If the tile is mostly empty or mostly nan, don't save it - if sumzero > nan_threshold * totalpix or sumnan > nan_threshold * totalpix: - return + if overlapping_crowns is not None: + return data, out_path_root, overlapping_crowns, minx, miny, buffer + + return data, out_path_root, None, minx, miny, buffer - out_meta = data.meta.copy() - out_meta.update({ - "driver": "GTiff", - "height": out_img.shape[1], - "width": out_img.shape[2], - "transform": out_transform, - "nodata": None, - }) - if dtype_bool: - out_meta.update({"dtype": "uint8"}) - - out_tif = out_path_root.with_suffix(out_path_root.suffix + ".tif") - with rasterio.open(out_tif, "w", **out_meta) as dest: - dest.write(out_img) - - clipped = rasterio.open(out_tif) - arr = clipped.read() - r, g, b = arr[0], arr[1], arr[2] - rgb = np.dstack((b, g, r)) - - # Rescale to 0-255 if necessary - if np.max(g) > 255: - rgb_rescaled = 255 * rgb / 65535 - else: - rgb_rescaled = rgb - - cv2.imwrite(str(out_path_root.with_suffix(out_path_root.suffix + ".png").resolve()), rgb_rescaled) - if overlapping_crowns is not None: - return data, out_path_root, overlapping_crowns, minx, miny, buffer + except RasterioIOError as e: + logger.error(f"RasterioIOError while applying mask {coords}: {e}") + return None From c2267505f31ca3956d3c1ba17ffcd1faf23076f3 Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Wed, 17 Jul 2024 10:43:34 +0000 Subject: [PATCH 16/63] refactor tiling --- detectree2/preprocessing/tiling.py | 218 ++++++++++++----------------- 1 file changed, 92 insertions(+), 126 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index d8940c7a..646d2fe8 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -87,120 +87,78 @@ def process_tile( Returns: None """ - data = rasterio.open(img_path) - - out_path = Path(out_dir) - out_path_root = out_path / f"{tilename}_{minx}_{miny}_{tile_width}_{buffer}_{crs}" + try: + with rasterio.open(img_path) as data: + out_path = Path(out_dir) + out_path_root = out_path / f"{tilename}_{minx}_{miny}_{tile_width}_{buffer}_{crs}" - minx_buffered = minx - buffer - miny_buffered = miny - buffer - maxx_buffered = minx + tile_width + buffer - maxy_buffered = miny + tile_height + buffer + minx_buffered = minx - buffer + miny_buffered = miny - buffer + maxx_buffered = minx + tile_width + buffer + maxy_buffered = miny + tile_height + buffer - bbox = box(minx_buffered, miny_buffered, maxx_buffered, maxy_buffered) - geo = gpd.GeoDataFrame({"geometry": bbox}, index=[0], crs=data.crs) - coords = get_features(geo) + bbox = box(minx_buffered, miny_buffered, maxx_buffered, maxy_buffered) + geo = gpd.GeoDataFrame({"geometry": bbox}, index=[0], crs=data.crs) + coords = get_features(geo) - overlapping_crowns = None - if crowns is not None: - overlapping_crowns = gpd.clip(crowns, geo) - if overlapping_crowns.empty or (overlapping_crowns.dissolve().area[0] / geo.area[0]) < threshold: - return + overlapping_crowns = None + if crowns is not None: + overlapping_crowns = gpd.clip(crowns, geo) + if overlapping_crowns.empty or (overlapping_crowns.dissolve().area[0] / geo.area[0]) < threshold: + return None - try: - with rasterio.open(img_path) as data: out_img, out_transform = mask(data, shapes=coords, crop=True) - out_sumbands = np.sum(out_img, axis=0) - zero_mask = np.where(out_sumbands == 0, 1, 0) - nan_mask = np.where(out_sumbands == 765, 1, 0) - sumzero = zero_mask.sum() - sumnan = nan_mask.sum() - totalpix = out_img.shape[1] * out_img.shape[2] - - # If the tile is mostly empty or mostly nan, don't save it - if sumzero > nan_threshold * totalpix or sumnan > nan_threshold * totalpix: - return None - - out_meta = data.meta.copy() - out_meta.update({ - "driver": "GTiff", - "height": out_img.shape[1], - "width": out_img.shape[2], - "transform": out_transform, - "nodata": None, - }) - if dtype_bool: - out_meta.update({"dtype": "uint8"}) - - out_tif = out_path_root.with_suffix(out_path_root.suffix + ".tif") - with rasterio.open(out_tif, "w", **out_meta) as dest: - dest.write(out_img) - - with rasterio.open(out_tif) as clipped: - arr = clipped.read() - r, g, b = arr[0], arr[1], arr[2] - rgb = np.dstack((b, g, r)) - - # Rescale to 0-255 if necessary - if np.max(g) > 255: - rgb_rescaled = 255 * rgb / 65535 - else: - rgb_rescaled = rgb - - cv2.imwrite(str(out_path_root.with_suffix(out_path_root.suffix + ".png").resolve()), rgb_rescaled) - - if overlapping_crowns is not None: - return data, out_path_root, overlapping_crowns, minx, miny, buffer - - return data, out_path_root, None, minx, miny, buffer + out_sumbands = np.sum(out_img, axis=0) + zero_mask = np.where(out_sumbands == 0, 1, 0) + nan_mask = np.where(out_sumbands == 765, 1, 0) + sumzero = zero_mask.sum() + sumnan = nan_mask.sum() + totalpix = out_img.shape[1] * out_img.shape[2] + + # If the tile is mostly empty or mostly nan, don't save it + if sumzero > nan_threshold * totalpix or sumnan > nan_threshold * totalpix: + return None + + out_meta = data.meta.copy() + out_meta.update({ + "driver": "GTiff", + "height": out_img.shape[1], + "width": out_img.shape[2], + "transform": out_transform, + "nodata": None, + }) + if dtype_bool: + out_meta.update({"dtype": "uint8"}) + + out_tif = out_path_root.with_suffix(".tif") + with rasterio.open(out_tif, "w", **out_meta) as dest: + dest.write(out_img) + + with rasterio.open(out_tif) as clipped: + arr = clipped.read() + r, g, b = arr[0], arr[1], arr[2] + rgb = np.dstack((b, g, r)) + + # Rescale to 0-255 if necessary + if np.max(g) > 255: + rgb_rescaled = 255 * rgb / 65535 + else: + rgb_rescaled = rgb + + cv2.imwrite(str(out_path_root.with_suffix(".png").resolve()), rgb_rescaled) + + if overlapping_crowns is not None: + return data, out_path_root, overlapping_crowns, minx, miny, buffer + + return data, out_path_root, None, minx, miny, buffer except RasterioIOError as e: logger.error(f"RasterioIOError while applying mask {coords}: {e}") return None - - - -def tile_data( - data: DatasetReader, - out_dir: str, - buffer: int = 30, - tile_width: int = 200, - tile_height: int = 200, - dtype_bool: bool = False, -) -> None: - """Tiles up orthomosaic for making predictions on. - - Tiles up full othomosaic into managable chunks to make predictions on. Use tile_data_train to generate tiled - training data. A bug exists on some input raster types whereby outputed tiles are completely black - the dtype_bool - argument should be switched if this is the case. - - Args: - data: Orthomosaic as a rasterio object in a UTM type projection - buffer: Overlapping buffer of tiles in meters (UTM) - tile_width: Tile width in meters - tile_height: Tile height in meters - dtype_bool: Flag to edit dtype to prevent black tiles - - Returns: - None - """ - out_path = Path(out_dir) - os.makedirs(out_path, exist_ok=True) - crs = CRS.from_string(data.crs.wkt).to_epsg() - tilename = Path(data.name).stem - total_tiles = int(((data.bounds[2] - data.bounds[0]) / tile_width) * ((data.bounds[3] - data.bounds[1]) / tile_height)) - - tile_args = [ - (data, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename) - for minx in np.arange(data.bounds[0], data.bounds[2] - tile_width, tile_width, int) - for miny in np.arange(data.bounds[1], data.bounds[3] - tile_height, tile_height, int) - ] - - with concurrent.futures.ThreadPoolExecutor() as executor: - list(executor.map(lambda args: process_tile(*args), tile_args)) - - print("Tiling complete") + except Exception as e: + logger.error(f"Error processing tile {tilename} at ({minx}, {miny}): {e}") + return None def process_tile_train( @@ -221,7 +179,7 @@ def process_tile_train( """Process a single tile for training data. Args: - data: Orthomosaic as a rasterio object in a UTM type projection + img_path: Path to the orthomosaic out_dir: Output directory buffer: Overlapping buffer of tiles in meters (UTM) tile_width: Tile width in meters @@ -238,17 +196,23 @@ def process_tile_train( Returns: None """ - data, out_path_root, overlapping_crowns, minx, miny, buffer = process_tile(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) + result = process_tile(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) + + if result is None: + #logger.warning(f"Skipping tile at ({minx}, {miny}) due to insufficient data.") + return + + data, out_path_root, overlapping_crowns, minx, miny, buffer = result overlapping_crowns = overlapping_crowns.explode(index_parts=True) moved = overlapping_crowns.translate(-minx + buffer, -miny + buffer) scalingx = 1 / (data.transform[0]) scalingy = -1 / (data.transform[4]) moved_scaled = moved.scale(scalingx, scalingy, origin=(0, 0)) - impath = {"imagePath": out_path_root.with_suffix(out_path_root.suffix + ".png").as_posix()} + impath = {"imagePath": out_path_root.with_suffix(".png").as_posix()} try: - filename = out_path_root.with_suffix(out_path_root.suffix + ".geojson") + filename = out_path_root.with_suffix(".geojson") moved_scaled = overlapping_crowns.set_geometry(moved_scaled) moved_scaled.to_file(driver="GeoJSON", filename=filename) with open(filename, "r") as f: @@ -257,10 +221,14 @@ def process_tile_train( with open(filename, "w") as f: json.dump(shp, f) except ValueError: - print("Cannot write empty DataFrame to file.") + logger.warning("Cannot write empty DataFrame to file.") return -def tile_data_train( # noqa: C901 +# Define a top-level helper function +def process_tile_train_helper(args): + return process_tile_train(*args) + +def tile_data( img_path: str, out_dir: str, buffer: int = 30, @@ -271,13 +239,14 @@ def tile_data_train( # noqa: C901 nan_threshold: float = 0.1, dtype_bool: bool = False, ) -> None: - """Tiles up orthomosaic and corresponding crowns into training tiles. + """Tiles up orthomosaic and corresponding crowns (if supplied) into training/prediction tiles. A threshold can be used to ensure a good coverage of crowns across a tile. Tiles that do not have sufficient coverage are rejected. Args: - data: Orthomosaic as a rasterio object in a UTM type projection + img_path: Path to the orthomosaic + out_dir: Output directory buffer: Overlapping buffer of tiles in meters (UTM) tile_width: Tile width in meters tile_height: Tile height in meters @@ -290,25 +259,22 @@ def tile_data_train( # noqa: C901 None """ - - # TODO: Clip data to crowns straight away to speed things up - # TODO: Tighten up epsg handling out_path = Path(out_dir) os.makedirs(out_path, exist_ok=True) tilename = Path(img_path).stem - data = rasterio.open(img_path) - crs = CRS.from_string(data.crs.wkt).to_epsg() + with rasterio.open(img_path) as data: + crs = data.crs.to_string() # Update CRS handling to avoid deprecated syntax - tile_args = [ - (img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) - for minx in np.arange(ceil(data.bounds[0]) + buffer, data.bounds[2] - tile_width - buffer, tile_width, int) - for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, int) - ] + tile_args = [ + (img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) + for minx in np.arange(ceil(data.bounds[0]) + buffer, data.bounds[2] - tile_width - buffer, tile_width, int) + for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, int) + ] - with concurrent.futures.ThreadPoolExecutor() as executor: - list(executor.map(lambda args: process_tile_train(*args), tile_args)) + with concurrent.futures.ProcessPoolExecutor() as executor: # Use ProcessPoolExecutor here + list(executor.map(process_tile_train_helper, tile_args)) - print("Tiling complete") + logger.info("Tiling complete") def image_details(fileroot): @@ -490,5 +456,5 @@ def to_traintest_folders( # noqa: C901 tile_width = 200 tile_height = 200 - tile_data_train(data, out_dir, buffer, tile_width, tile_height, crowns) + tile_data(data, out_dir, buffer, tile_width, tile_height, crowns) to_traintest_folders(folds=5) From eb163bec959fd4458d80fa6866538113ef50655e Mon Sep 17 00:00:00 2001 From: Ball JGC Date: Thu, 8 Aug 2024 12:56:24 +0000 Subject: [PATCH 17/63] ms tiling --- detectree2/preprocessing/tiling.py | 128 +++++++++++++++++++++++++++-- docs/source/tutorial.rst | 31 +++---- 2 files changed, 139 insertions(+), 20 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 646d2fe8..46510cc8 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -160,6 +160,112 @@ def process_tile( logger.error(f"Error processing tile {tilename} at ({minx}, {miny}): {e}") return None +def process_tile_ms( + img_path: str, + out_dir: str, + buffer: int, + tile_width: int, + tile_height: int, + dtype_bool: bool, + minx, + miny, + crs, + tilename, + crowns: gpd.GeoDataFrame = None, + threshold: float = 0, + nan_threshold: float = 0, +): + """Process a single tile for making predictions. + + Args: + img_path: Path to the orthomosaic + out_dir: Output directory + buffer: Overlapping buffer of tiles in meters (UTM) + tile_width: Tile width in meters + tile_height: Tile height in meters + dtype_bool: Flag to edit dtype to prevent black tiles + minx: Minimum x coordinate of tile + miny: Minimum y coordinate of tile + crs: Coordinate reference system + tilename: Name of the tile + + Returns: + None + """ + try: + with rasterio.open(img_path) as data: + out_path = Path(out_dir) + out_path_root = out_path / f"{tilename}_{minx}_{miny}_{tile_width}_{buffer}_{crs}" + + minx_buffered = minx - buffer + miny_buffered = miny - buffer + maxx_buffered = minx + tile_width + buffer + maxy_buffered = miny + tile_height + buffer + + bbox = box(minx_buffered, miny_buffered, maxx_buffered, maxy_buffered) + geo = gpd.GeoDataFrame({"geometry": [bbox]}, index=[0], crs=data.crs) + coords = [geo.geometry[0].__geo_interface__] + + overlapping_crowns = None + if crowns is not None: + overlapping_crowns = gpd.clip(crowns, geo) + if overlapping_crowns.empty or (overlapping_crowns.dissolve().area[0] / geo.area[0]) < threshold: + return None + + out_img, out_transform = mask(data, shapes=coords, crop=True) + + out_sumbands = np.sum(out_img, axis=0) + zero_mask = np.where(out_sumbands == 0, 1, 0) + nan_mask = np.isnan(out_sumbands) + sumzero = zero_mask.sum() + sumnan = nan_mask.sum() + totalpix = out_img.shape[1] * out_img.shape[2] + + # If the tile is mostly empty or mostly nan, don't save it + if sumzero > nan_threshold * totalpix or sumnan > nan_threshold * totalpix: + return None + + out_meta = data.meta.copy() + out_meta.update({ + "driver": "GTiff", + "height": out_img.shape[1], + "width": out_img.shape[2], + "transform": out_transform, + "nodata": None, + }) + if dtype_bool: + out_meta.update({"dtype": "uint8"}) + + out_tif = out_path_root.with_suffix(".tif") + with rasterio.open(out_tif, "w", **out_meta) as dest: + dest.write(out_img) + + # Save all bands as an image if needed (not just the first 3 bands) + band_images = [] + for band_index in range(out_img.shape[0]): + band_image = out_img[band_index, :, :] + if np.max(band_image) > 255: + band_image = 255 * band_image / np.max(band_image) + band_images.append(band_image.astype(np.uint8)) + + # Stack the bands into a single image array + full_image = np.stack(band_images, axis=-1) + + # Save the full image with potentially more than 3 bands + full_image_path = out_path_root.with_suffix(".png") + cv2.imwrite(str(full_image_path.resolve()), full_image) + + if overlapping_crowns is not None: + return data, out_path_root, overlapping_crowns, minx, miny, buffer + + return data, out_path_root, None, minx, miny, buffer + + except RasterioIOError as e: + logger.error(f"RasterioIOError while applying mask {coords}: {e}") + return None + except Exception as e: + logger.error(f"Error processing tile {tilename} at ({minx}, {miny}): {e}") + return None def process_tile_train( img_path: str, @@ -174,7 +280,8 @@ def process_tile_train( tilename, crowns: gpd.GeoDataFrame, threshold, - nan_threshold + nan_threshold, + mode: str = "rgb", ) -> None: """Process a single tile for training data. @@ -196,7 +303,12 @@ def process_tile_train( Returns: None """ - result = process_tile(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) + if mode == "rgb": + result = process_tile(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, + crowns, threshold, nan_threshold) + elif mode == "ms": + result = process_tile_ms(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, + crowns, threshold, nan_threshold) if result is None: #logger.warning(f"Skipping tile at ({minx}, {miny}) due to insufficient data.") @@ -241,8 +353,10 @@ def tile_data( ) -> None: """Tiles up orthomosaic and corresponding crowns (if supplied) into training/prediction tiles. - A threshold can be used to ensure a good coverage of crowns across a tile. Tiles that do not have sufficient - coverage are rejected. + Tiles up large rasters into managable tiles for training and prediction. If crowns are not supplied the function + will tile up the entire landscape for prediction. If crowns are supplied the function will tile these with the image + and skip tiles without a minimum coverage of crowns. The 'threshold' can be varied to ensure a good coverage of + crowns across a traing tile. Tiles that do not have sufficient coverage are skipped. Args: img_path: Path to the orthomosaic @@ -266,9 +380,11 @@ def tile_data( crs = data.crs.to_string() # Update CRS handling to avoid deprecated syntax tile_args = [ - (img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) + (img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, + threshold, nan_threshold) for minx in np.arange(ceil(data.bounds[0]) + buffer, data.bounds[2] - tile_width - buffer, tile_width, int) - for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, int) + for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, + int) ] with concurrent.futures.ProcessPoolExecutor() as executor: # Use ProcessPoolExecutor here diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index 1af9c89a..5095033f 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -69,7 +69,7 @@ We call functions to from ``detectree2``'s tiling and training modules. .. code-block:: python - from detectree2.preprocessing.tiling import tile_data_train, to_traintest_folders + from detectree2.preprocessing.tiling import tile_data, to_traintest_folders from detectree2.models.train import register_train_data, MyTrainer, setup_cfg import rasterio import geopandas as gpd @@ -112,22 +112,24 @@ The tile size will depend on: The total tile size here is 100 m x 100 m (a 40 m x 40 m core area with a surrounding 30 m buffer that overlaps with surrounding tiles). Including a buffer is recommended as it allows for tiles that include more training crowns. -Next we tile the data. The ``tile_data_train`` function will only retain tiles that contain more than the given -``threshold`` coverage of training data (here 60%). This helps to reduce the chance that the network is trained with -tiles that contain a large number of unlabelled crowns (which would reduce its sensitivity). +Next we tile the data. The ``tile_data`` function, when ``crowns`` is supplied, will only retain tiles that contain more +than the given ``threshold`` coverage of training data (here 60%). This helps to reduce the chance that the network is +trained with tiles that contain a large number of unlabelled crowns (which would reduce its sensitivity). .. code-block:: python - tile_data_train(data, out_dir, buffer, tile_width, tile_height, crowns, threshold) + tile_data(img_path, out_dir, buffer, tile_width, tile_height, crowns, threshold) .. warning:: - If tiles are outputing as blank images set ``dtype_bool = True`` in the ``tile_data_train`` function. This is a bug - and we are working on fixing it. + If tiles are outputing as blank images set ``dtype_bool = True`` in the ``tile_data`` function. This is a bug + and we are working on fixing it. Supplying crown polygons will cause the function to tile for + training (as opposed to landscape prediction which is described below). .. note:: - You will want to relax the ``threshold`` value if your trees are sparsely distributed across your landscape. - Remember, ``detectree2`` was initially designed for dense, closed canopy forests so some of the default assumptions - will reflect that. + You will want to relax the ``threshold`` value if your trees are sparsely distributed across your landscape or if you + want to include non-forest areas (e.g. river, roads). Remember, ``detectree2`` was initially designed for dense, + closed canopy forests so some of the default assumptions will reflect that and parameters will need to be adjusted + for different systems. Send geojsons to train folder (with sub-folders for k-fold cross validation) and test folder. @@ -141,7 +143,7 @@ Send geojsons to train folder (with sub-folders for k-fold cross validation) and 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). + overlap in the buffers between test and train/val tiles). The data has now been tiled and partitioned for model training, tuning and evaluation. @@ -161,8 +163,8 @@ The data has now been tiled and partitioned for model training, tuning and evalu └── test (test data folder) -It is advisable to do a visual inspection on the tiles to ensure that the tiling has worked as expected and that crowns -and images align. This can be done quickly with the inbuilt ``detectron2`` visualisation tools. +It is recommended to visually inspect the tiles before training to ensure that the tiling has worked as expected and +that crowns and images align. This can be done quickly with the inbuilt ``detectron2`` visualisation tools. .. code-block:: python @@ -325,7 +327,8 @@ can discard partial the crowns predicted at the edge of tiles. .. warning:: If tiles are outputing as blank images set ``dtype_bool = True`` in the ``tile_data`` function. This is a bug - and we are working on fixing it. + and we are working on fixing it. Avoid supplying crown polygons otherwise the function will run as if it is tiling + for training. To download a pre-trained model from the ``model_garden`` you can run ``wget`` on the package repo From 6441ae7eb54e90a6bd960465deda58bbfc362c2f Mon Sep 17 00:00:00 2001 From: James Ball Date: Fri, 9 Aug 2024 13:51:10 +0100 Subject: [PATCH 18/63] tiling mode added --- detectree2/preprocessing/tiling.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 46510cc8..502982f7 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -138,7 +138,7 @@ def process_tile( with rasterio.open(out_tif) as clipped: arr = clipped.read() r, g, b = arr[0], arr[1], arr[2] - rgb = np.dstack((b, g, r)) + rgb = np.dstack((b, g, r)) # Reorder for cv2 (BGRA) # Rescale to 0-255 if necessary if np.max(g) > 255: @@ -249,6 +249,7 @@ def process_tile_ms( band_images.append(band_image.astype(np.uint8)) # Stack the bands into a single image array + # Does the band order need to be reversed as in the RGB case? full_image = np.stack(band_images, axis=-1) # Save the full image with potentially more than 3 bands @@ -350,6 +351,7 @@ def tile_data( threshold: float = 0, nan_threshold: float = 0.1, dtype_bool: bool = False, + mode: str = "rgb", ) -> None: """Tiles up orthomosaic and corresponding crowns (if supplied) into training/prediction tiles. @@ -381,7 +383,7 @@ def tile_data( tile_args = [ (img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, - threshold, nan_threshold) + threshold, nan_threshold, mode) for minx in np.arange(ceil(data.bounds[0]) + buffer, data.bounds[2] - tile_width - buffer, tile_width, int) for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, int) From dc8e0a3541a085c3a818d19e9c666ddb2ea65343 Mon Sep 17 00:00:00 2001 From: James Ball Date: Fri, 9 Aug 2024 14:19:02 +0100 Subject: [PATCH 19/63] remove lfs --- .gitattributes | 1 - 1 file changed, 1 deletion(-) diff --git a/.gitattributes b/.gitattributes index ec4a626f..e69de29b 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1 +0,0 @@ -*.pth filter=lfs diff=lfs merge=lfs -text From e71135ea746d649bc4bf34590ca4aa1bff95b0d0 Mon Sep 17 00:00:00 2001 From: James Ball Date: Tue, 20 Aug 2024 15:37:48 +0100 Subject: [PATCH 20/63] ms bands --- detectree2/data_loading/custom.py | 73 ++++++++++++++++++++++++++++++ detectree2/models/train.py | 47 +++++++++++++++++-- detectree2/preprocessing/tiling.py | 28 ++++++++---- 3 files changed, 134 insertions(+), 14 deletions(-) create mode 100644 detectree2/data_loading/custom.py diff --git a/detectree2/data_loading/custom.py b/detectree2/data_loading/custom.py new file mode 100644 index 00000000..c1e37c82 --- /dev/null +++ b/detectree2/data_loading/custom.py @@ -0,0 +1,73 @@ +import rasterio +import numpy as np +import torch +from torch.utils.data import Dataset +import detectron2.data.transforms as T +from detectron2.structures import BoxMode, Instances, BitMasks +import cv2 + +class CustomTIFFDataset(Dataset): + def __init__(self, annotations, transforms=None): + """ + Args: + annotations (list): List of dictionaries containing image file paths and annotations. + transforms (callable, optional): Optional transform to be applied on a sample. + """ + self.annotations = annotations + self.transforms = transforms + + def __len__(self): + return len(self.annotations) + + def __getitem__(self, idx): + # Load the TIFF image + img_info = self.annotations[idx] + with rasterio.open(img_info['file_name']) as src: + # Read all bands (assuming they are all needed) + image = src.read() + # Normalize or rescale if necessary + image = image.astype(np.float32) / 255.0 # Example normalization + # If the number of bands is not 3, reduce to 3 or handle accordingly + #if image.shape[0] > 3: + # image = image[:3, :, :] # Taking the first 3 bands (e.g., RGB) + # Convert to HWC format expected by Detectron2 + #image = np.transpose(image, (1, 2, 0)) + + # Prepare annotations (this part needs to be adapted to your specific annotations) + target = { + "image_id": idx, + "annotations": img_info["annotations"], + "width": img_info["width"], + "height": img_info["height"], + } + + if self.transforms is not None: + augmentations = T.AugmentationList(self.transforms) + image, target = augmentations(image, target) + + # Convert to Detectron2-compatible format + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + instances = self.get_detectron_instances(target) + + return image, instances + + def get_detectron_instances(self, target): + """ + Converts annotations into Detectron2's format. + This example assumes annotations are in COCO format, and you'll need to adapt it for your needs. + """ + boxes = [obj["bbox"] for obj in target["annotations"]] + boxes = torch.as_tensor(boxes, dtype=torch.float32) + boxes = BoxMode.convert(boxes, BoxMode.XYWH_ABS, BoxMode.XYXY_ABS) + + # Create BitMasks from the binary mask data (assuming the mask is a binary numpy array) + masks = [obj["segmentation"] for obj in target["annotations"]] # Replace with actual mask loading + masks = BitMasks(torch.stack([torch.from_numpy(mask) for mask in masks])) + + instances = Instances( + image_size=(target["height"], target["width"]), + gt_boxes=boxes, + gt_classes=torch.tensor([obj["category_id"] for obj in target["annotations"]], dtype=torch.int64), + gt_masks=masks + ) + return instances diff --git a/detectree2/models/train.py b/detectree2/models/train.py index d7606793..e7022618 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -42,6 +42,39 @@ # from PIL import Image +class MultiBandDatasetMapper: + def __init__(self, cfg, is_train=True, augmentations=None): + self.is_train = is_train + self.augmentations = T.AugmentationList(augmentations) if augmentations else None + + def __call__(self, dataset_dict): + dataset_dict = dataset_dict.copy() # Make a copy of the dataset dict + image = utils.read_image(dataset_dict["file_name"], format="BGR") # This reads the image + image = self.load_all_bands(dataset_dict["file_name"]) # Custom method to load all bands + + if self.augmentations: + image, transforms = T.apply_augmentations(self.augmentations, image) + dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) + else: + dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) + + annos = [ + utils.transform_instance_annotations(annotation, transforms, image.shape[:2]) + for annotation in dataset_dict.pop("annotations") + ] + dataset_dict["instances"] = utils.annotations_to_instances(annos, image.shape[:2]) + return dataset_dict + + def load_all_bands(self, image_path): + """Load all bands of the image using rasterio and return as a numpy array.""" + with rasterio.open(image_path) as src: + image = src.read() # This will read all bands + # Normalize the bands if necessary + image = image.astype(np.float32) / 255.0 + # Transpose to HWC format + image = np.transpose(image, (1, 2, 0)) + return image + class LossEvalHook(HookBase): """Do inference and get the loss metric. @@ -244,7 +277,7 @@ def build_hooks(self): build_detection_test_loader( self.cfg, self.cfg.DATASETS.TEST, - DatasetMapper(self.cfg, True) + DatasetMapper(self.cfg, True) # Need to edit this for custom dataset ), self.patience, ), @@ -331,7 +364,13 @@ def get_tree_dicts(directory: str, classes: List[str] = None, classes_at: str = filename = img_anns["imagePath"] # Make sure we have the correct height and width - height, width = cv2.imread(filename).shape[:2] + # If image path ends in .png use cv2 to get height and width + if filename.endswith(".png"): + height, width = cv2.imread(filename).shape[:2] # Need to change this to rasterio + # else if image path ends in .tif use rasterio to get height and width + elif filename.endswith(".tif"): + with rasterio.open(filename) as src: + height, width = src.shape record["file_name"] = filename record["height"] = height @@ -459,9 +498,9 @@ def register_train_data(train_location, def read_data(out_dir): """Function that will read the classes that are recorded during tiling.""" list = [] - out_tif = out_dir + 'classes.txt' + classes_txt = out_dir + 'classes.txt' # open file and read the content in a list - with open(out_tif, 'r') as fp: + with open(classes_txt, 'r') as fp: for line in fp: # remove linebreak from a current name # linebreak is the last character of each line diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 502982f7..73f42cc7 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -240,21 +240,22 @@ def process_tile_ms( with rasterio.open(out_tif, "w", **out_meta) as dest: dest.write(out_img) + # Images withmore than 4 bands are not supported by cv2 # Save all bands as an image if needed (not just the first 3 bands) - band_images = [] - for band_index in range(out_img.shape[0]): - band_image = out_img[band_index, :, :] - if np.max(band_image) > 255: - band_image = 255 * band_image / np.max(band_image) - band_images.append(band_image.astype(np.uint8)) + #band_images = [] + #for band_index in range(out_img.shape[0]): + # band_image = out_img[band_index, :, :] + # if np.max(band_image) > 255: + # band_image = 255 * band_image / np.max(band_image) + # band_images.append(band_image.astype(np.uint8)) # Stack the bands into a single image array # Does the band order need to be reversed as in the RGB case? - full_image = np.stack(band_images, axis=-1) + #full_image = np.stack(band_images, axis=-1) # Save the full image with potentially more than 3 bands - full_image_path = out_path_root.with_suffix(".png") - cv2.imwrite(str(full_image_path.resolve()), full_image) + #full_image_path = out_path_root.with_suffix(".png") + #cv2.imwrite(str(full_image_path.resolve()), full_image) if overlapping_crowns is not None: return data, out_path_root, overlapping_crowns, minx, miny, buffer @@ -322,7 +323,11 @@ def process_tile_train( scalingx = 1 / (data.transform[0]) scalingy = -1 / (data.transform[4]) moved_scaled = moved.scale(scalingx, scalingy, origin=(0, 0)) - impath = {"imagePath": out_path_root.with_suffix(".png").as_posix()} + + if mode == "rgb": + impath = {"imagePath": out_path_root.with_suffix(".png").as_posix()} + elif mode == "ms": + impath = {"imagePath": out_path_root.with_suffix(".tif").as_posix()} try: filename = out_path_root.with_suffix(".geojson") @@ -455,6 +460,9 @@ def record_data(crowns, list_of_classes = crowns[column].unique().tolist() + # Sort the list of classes in alphabetical order + list_of_classes.sort() + print("**The list of classes are:**") print(list_of_classes) print("**The list has been saved to the out_dir**") From a5e7088ba72f4dd0f703e69a51cee55f14f9455e Mon Sep 17 00:00:00 2001 From: James Ball Date: Tue, 20 Aug 2024 18:23:29 +0100 Subject: [PATCH 21/63] ms bands --- detectree2/models/train.py | 63 ++++++++++++++++++++++++++++---------- 1 file changed, 47 insertions(+), 16 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index e7022618..034fb2b5 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -17,6 +17,7 @@ import detectron2.data.transforms as T # noqa:N812 import detectron2.utils.comm as comm import numpy as np +import rasterio import torch from detectron2 import model_zoo from detectron2.checkpoint import DetectionCheckpointer # noqa:F401 @@ -28,6 +29,7 @@ build_detection_test_loader, build_detection_train_loader, ) +from detectron2.data import detection_utils as utils from detectron2.engine import DefaultTrainer from detectron2.engine.hooks import HookBase from detectron2.evaluation import COCOEvaluator, verify_results @@ -42,15 +44,28 @@ # from PIL import Image -class MultiBandDatasetMapper: +class FlexibleDatasetMapper: def __init__(self, cfg, is_train=True, augmentations=None): + self.cfg = cfg self.is_train = is_train self.augmentations = T.AugmentationList(augmentations) if augmentations else None + self.rgb_mapper = DatasetMapper(cfg, is_train=is_train, augmentations=self.augmentations) def __call__(self, dataset_dict): + # Determine the number of bands + with rasterio.open(dataset_dict["file_name"]) as src: + num_bands = src.count + + if num_bands == 3: + # Use the standard RGB DatasetMapper + return self.rgb_mapper(dataset_dict) + else: + # Use the custom multi-band mapper + return self.multi_band_mapper(dataset_dict) + + def multi_band_mapper(self, dataset_dict): dataset_dict = dataset_dict.copy() # Make a copy of the dataset dict - image = utils.read_image(dataset_dict["file_name"], format="BGR") # This reads the image - image = self.load_all_bands(dataset_dict["file_name"]) # Custom method to load all bands + image = self.load_image(dataset_dict["file_name"]) # Custom method to load image (multi-band) if self.augmentations: image, transforms = T.apply_augmentations(self.augmentations, image) @@ -58,6 +73,7 @@ def __call__(self, dataset_dict): else: dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) + # Transform annotations annos = [ utils.transform_instance_annotations(annotation, transforms, image.shape[:2]) for annotation in dataset_dict.pop("annotations") @@ -65,14 +81,12 @@ def __call__(self, dataset_dict): dataset_dict["instances"] = utils.annotations_to_instances(annos, image.shape[:2]) return dataset_dict - def load_all_bands(self, image_path): - """Load all bands of the image using rasterio and return as a numpy array.""" + def load_image(self, image_path): + """Load multi-band image.""" with rasterio.open(image_path) as src: - image = src.read() # This will read all bands - # Normalize the bands if necessary - image = image.astype(np.float32) / 255.0 - # Transpose to HWC format - image = np.transpose(image, (1, 2, 0)) + image = src.read() # This will read all bands (multi-band support) + image = image.transpose(1, 2, 0) # Convert to HWC format + image = image.astype(np.float32) # Convert to float32 for consistency return image class LossEvalHook(HookBase): @@ -286,7 +300,7 @@ def build_hooks(self): def build_train_loader(cls, cfg): - """Summary. + """Build the train loader with flexibility for RGB and multi-band images. Args: cfg (_type_): _description_ @@ -307,22 +321,39 @@ def build_train_loader(cls, cfg): if cfg.RESIZE: augmentations.append(T.Resize((1000, 1000))) elif cfg.RESIZE == "random": + size = None for i, datas in enumerate(DatasetCatalog.get(cfg.DATASETS.TRAIN[0])): location = datas['file_name'] - size = cv2.imread(location).shape[0] + try: + # Try to read with cv2 (for RGB images) + img = cv2.imread(location) + if img is not None: + size = img.shape[0] + else: + # Fall back to rasterio for multi-band images + with rasterio.open(location) as src: + size = src.height # Assuming square images + except Exception as e: + # Handle any errors that occur during loading + print(f"Error loading image {location}: {e}") + continue break - print("ADD RANDOM RESIZE WITH SIZE = ", size) - augmentations.append(T.ResizeScale(0.6, 1.4, size, size)) + + if size: + print("ADD RANDOM RESIZE WITH SIZE = ", size) + augmentations.append(T.ResizeScale(0.6, 1.4, size, size)) + else: + raise ValueError("Failed to determine image size for random resize") + return build_detection_train_loader( cfg, - mapper=DatasetMapper( + mapper=FlexibleDatasetMapper( cfg, is_train=True, augmentations=augmentations, ), ) - def get_tree_dicts(directory: str, classes: List[str] = None, classes_at: str = None) -> List[Dict]: """Get the tree dictionaries. From 2042c482d7b6695df304ec12cb9e991025a5a45c Mon Sep 17 00:00:00 2001 From: James Ball Date: Tue, 20 Aug 2024 18:49:02 +0100 Subject: [PATCH 22/63] ms bands --- detectree2/preprocessing/tiling.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 73f42cc7..bafd259a 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -509,7 +509,8 @@ def to_traintest_folders( # noqa: C901 Path(out_dir / "train").mkdir(parents=True, exist_ok=True) Path(out_dir / "test").mkdir(parents=True, exist_ok=True) - file_names = tiles_dir.glob("*.png") + #file_names = tiles_dir.glob("*.png") + file_names = tiles_dir.glob("*.geojson") file_roots = [item.stem for item in file_names] num = list(range(0, len(file_roots))) From f3d1c89341a2cfa9648a2f77db4c0cb8da62158e Mon Sep 17 00:00:00 2001 From: James Ball Date: Wed, 21 Aug 2024 18:53:34 +0100 Subject: [PATCH 23/63] ms bands --- detectree2/models/train.py | 179 +++++++++++++++++-------------------- 1 file changed, 84 insertions(+), 95 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 034fb2b5..85d23e35 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -43,51 +43,42 @@ # from IPython.display import display # from PIL import Image - class FlexibleDatasetMapper: def __init__(self, cfg, is_train=True, augmentations=None): self.cfg = cfg self.is_train = is_train - self.augmentations = T.AugmentationList(augmentations) if augmentations else None - self.rgb_mapper = DatasetMapper(cfg, is_train=is_train, augmentations=self.augmentations) + self.rgb_mapper = DatasetMapper(cfg, is_train=is_train) def __call__(self, dataset_dict): - # Determine the number of bands - with rasterio.open(dataset_dict["file_name"]) as src: - num_bands = src.count - + # First, try to open the image using rasterio to determine the number of bands + try: + with rasterio.open(dataset_dict["file_name"]) as src: + num_bands = src.count + except rasterio.errors.RasterioIOError as e: + raise ValueError(f"Error opening image file: {dataset_dict['file_name']}") from e + + # If the image has 3 bands, use the standard RGB DatasetMapper if num_bands == 3: - # Use the standard RGB DatasetMapper return self.rgb_mapper(dataset_dict) else: - # Use the custom multi-band mapper + # Otherwise, handle the image with the custom multi-band mapper return self.multi_band_mapper(dataset_dict) def multi_band_mapper(self, dataset_dict): dataset_dict = dataset_dict.copy() # Make a copy of the dataset dict - image = self.load_image(dataset_dict["file_name"]) # Custom method to load image (multi-band) - - if self.augmentations: - image, transforms = T.apply_augmentations(self.augmentations, image) - dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) - else: - dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) + with rasterio.open(dataset_dict["file_name"]) as src: + img = src.read() # Read all bands + img = np.transpose(img, (1, 2, 0)) # Convert to HWC format - # Transform annotations - annos = [ - utils.transform_instance_annotations(annotation, transforms, image.shape[:2]) - for annotation in dataset_dict.pop("annotations") - ] - dataset_dict["instances"] = utils.annotations_to_instances(annos, image.shape[:2]) + dataset_dict["image"] = torch.as_tensor(img.transpose(2, 0, 1).astype("float32")) + + # Ensure ground truth instances (annotations) are included + annos = dataset_dict.pop("annotations", []) + instances = utils.annotations_to_instances(annos, dataset_dict["image"].shape[1:]) + dataset_dict["instances"] = utils.filter_empty_instances(instances) + return dataset_dict - def load_image(self, image_path): - """Load multi-band image.""" - with rasterio.open(image_path) as src: - image = src.read() # This will read all bands (multi-band support) - image = image.transpose(1, 2, 0) # Convert to HWC format - image = image.astype(np.float32) # Convert to float32 for consistency - return image class LossEvalHook(HookBase): """Do inference and get the loss metric. @@ -202,6 +193,9 @@ def after_step(self): self.trainer.storage.put_scalars(timetest=12) def after_train(self): + if not self.trainer.APs: + print("No APs were recorded during training. Skipping model selection.") + return # Select the model with the best AP50 index = self.trainer.APs.index(max(self.trainer.APs)) + 1 # Error in demo: @@ -225,8 +219,9 @@ class MyTrainer(DefaultTrainer): def __init__(self, cfg, patience): # noqa: D107 self.patience = patience - # self.resize = resize super().__init__(cfg) + #self.data_loader = build_train_loader(cfg) # Use custom data loader + def train(self): """Run training. @@ -291,68 +286,71 @@ def build_hooks(self): build_detection_test_loader( self.cfg, self.cfg.DATASETS.TEST, - DatasetMapper(self.cfg, True) # Need to edit this for custom dataset + FlexibleDatasetMapper(self.cfg, True) # Need to edit this for custom dataset ), self.patience, ), ) return hooks + @classmethod + def build_train_loader(cls, cfg): + """Build the train loader with flexibility for RGB and multi-band images. -def build_train_loader(cls, cfg): - """Build the train loader with flexibility for RGB and multi-band images. + Args: + cfg (_type_): _description_ - Args: - cfg (_type_): _description_ + Returns: + _type_: _description_ + """ + augmentations = [ + T.RandomRotation(angle=[90, 90], expand=False), + T.RandomLighting(0.7), + T.RandomFlip(prob=0.4, horizontal=True, vertical=False), + T.RandomFlip(prob=0.4, horizontal=False, vertical=True), + ] - Returns: - _type_: _description_ - """ - augmentations = [ - T.RandomBrightness(0.8, 1.8), - T.RandomContrast(0.6, 1.3), - T.RandomSaturation(0.8, 1.4), - T.RandomRotation(angle=[90, 90], expand=False), - T.RandomLighting(0.7), - T.RandomFlip(prob=0.4, horizontal=True, vertical=False), - T.RandomFlip(prob=0.4, horizontal=False, vertical=True), - ] - - if cfg.RESIZE: - augmentations.append(T.Resize((1000, 1000))) - elif cfg.RESIZE == "random": - size = None - for i, datas in enumerate(DatasetCatalog.get(cfg.DATASETS.TRAIN[0])): - location = datas['file_name'] - try: - # Try to read with cv2 (for RGB images) - img = cv2.imread(location) - if img is not None: - size = img.shape[0] - else: - # Fall back to rasterio for multi-band images - with rasterio.open(location) as src: - size = src.height # Assuming square images - except Exception as e: - # Handle any errors that occur during loading - print(f"Error loading image {location}: {e}") - continue - break - - if size: - print("ADD RANDOM RESIZE WITH SIZE = ", size) - augmentations.append(T.ResizeScale(0.6, 1.4, size, size)) - else: - raise ValueError("Failed to determine image size for random resize") + # Some augmentations are only applicable to 3-band images + if cfg.IMGMODE == "rgb": + augmentations.append(T.RandomBrightness(0.8, 1.8), + T.RandomContrast(0.6, 1.3), + T.RandomSaturation(0.8, 1.4)) + + if cfg.RESIZE: + augmentations.append(T.Resize((1000, 1000))) + elif cfg.RESIZE == "random": + size = None + for i, datas in enumerate(DatasetCatalog.get(cfg.DATASETS.TRAIN[0])): + location = datas['file_name'] + try: + # Try to read with cv2 (for RGB images) + img = cv2.imread(location) + if img is not None: + size = img.shape[0] + else: + # Fall back to rasterio for multi-band images + with rasterio.open(location) as src: + size = src.height # Assuming square images + except Exception as e: + # Handle any errors that occur during loading + print(f"Error loading image {location}: {e}") + continue + break + + if size: + print("ADD RANDOM RESIZE WITH SIZE = ", size) + augmentations.append(T.ResizeScale(0.6, 1.4, size, size)) + else: + raise ValueError("Failed to determine image size for random resize") - return build_detection_train_loader( - cfg, - mapper=FlexibleDatasetMapper( + return build_detection_train_loader( cfg, - is_train=True, - augmentations=augmentations, - ), - ) + mapper=FlexibleDatasetMapper( + cfg, + is_train=True, + augmentations=augmentations, + ), + ) def get_tree_dicts(directory: str, classes: List[str] = None, classes_at: str = None) -> List[Dict]: """Get the tree dictionaries. @@ -366,23 +364,13 @@ def get_tree_dicts(directory: str, classes: List[str] = None, classes_at: str = List of dictionaries corresponding to segmentations of trees. Each dictionary includes bounding box around tree and points tracing a polygon around a tree. """ - # filepath = '/content/drive/MyDrive/forestseg/paracou_data/Panayiotis_Outputs/220303_AllSpLabelled.gpkg' - # datagpd = gpd.read_file(filepath) - # List_Genus = datagpd.Genus_Species.to_list() - # Genus_Species_UniqueList = list(set(List_Genus)) - - # if classes is not None: # list_of_classes = crowns[variable].unique().tolist() classes = classes else: classes = ["tree"] - # classes = Genus_Species_UniqueList #['tree'] # genus_species list + dataset_dicts = [] - # for root, dirs, files in os.walk(train_location): - # for file in files: - # if file.endswith(".geojson"): - # print(os.path.join(root, file)) for filename in [file for file in os.listdir(directory) if file.endswith(".geojson")]: json_file = os.path.join(directory, filename) @@ -395,10 +383,9 @@ def get_tree_dicts(directory: str, classes: List[str] = None, classes_at: str = filename = img_anns["imagePath"] # Make sure we have the correct height and width - # If image path ends in .png use cv2 to get height and width + # If image path ends in .png use cv2 to get height and width else if image path ends in .tif use rasterio if filename.endswith(".png"): - height, width = cv2.imread(filename).shape[:2] # Need to change this to rasterio - # else if image path ends in .tif use rasterio to get height and width + height, width = cv2.imread(filename).shape[:2] elif filename.endswith(".tif"): with rasterio.open(filename) as src: height, width = src.shape @@ -587,6 +574,7 @@ def setup_cfg( eval_period=100, out_dir="./train_outputs", resize=True, + imgmode="rgb", ): """Set up config object # noqa: D417. @@ -636,6 +624,7 @@ def setup_cfg( cfg.TEST.EVAL_PERIOD = eval_period cfg.RESIZE = resize cfg.INPUT.MIN_SIZE_TRAIN = 1000 + cfg.IMGMODE = imgmode return cfg From c8803bd38327e7cc4c8954be7d8fd90c031c5575 Mon Sep 17 00:00:00 2001 From: James Ball Date: Thu, 22 Aug 2024 17:15:28 +0100 Subject: [PATCH 24/63] dataset mapper updates --- detectree2/models/train.py | 169 ++++++++++++++++++++++++----- detectree2/preprocessing/tiling.py | 17 --- 2 files changed, 140 insertions(+), 46 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 85d23e35..3855c876 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -43,42 +43,151 @@ # from IPython.display import display # from PIL import Image -class FlexibleDatasetMapper: +#class FlexibleDatasetMapper: +# def __init__(self, cfg, is_train=True, augmentations=None): +# self.cfg = cfg +# self.is_train = is_train +# # Ensure augmentations is always a list +# if augmentations is None: +# augmentations = [] +# self.augmentations = augmentations +# self.rgb_mapper = DatasetMapper(cfg, is_train=is_train, augmentations=augmentations) +# +# def __call__(self, dataset_dict): +# # First, try to open the image using rasterio to determine the number of bands +# try: +# with rasterio.open(dataset_dict["file_name"]) as src: +# num_bands = src.count +# except rasterio.errors.RasterioIOError as e: +# raise ValueError(f"Error opening image file: {dataset_dict['file_name']}") from e +# +# # If the image has 3 bands, use the standard RGB DatasetMapper +# if num_bands == 3: +# print("Using RGB mapper") +# return self.rgb_mapper(dataset_dict) +# else: +# # Otherwise, handle the image with the custom multi-band mapper +# print("Using multi-band mapper") +# return self.multi_band_mapper(dataset_dict) +# +# def multi_band_mapper(self, dataset_dict): +# dataset_dict = dataset_dict.copy() # Make a copy of the dataset dict +# +# # Load the image using rasterio +# with rasterio.open(dataset_dict["file_name"]) as src: +# img = src.read() # Read all bands +# img = np.transpose(img, (1, 2, 0)) # Convert to HWC format (Height, Width, Channels) +# +# # Convert the image to float32 +# img = img.astype("float32") +# +# # Normalize the image if necessary (optional step) +# # Example: normalize to [0, 1] if the original image has high dynamic range values +# img /= 65535.0 # Assuming the original range is [0, 65535] for 16-bit images +# +# # Apply augmentations if provided +# if self.augmentations: +# aug_input = T.AugInput(img) +# all_transforms = [] +# +# # Apply each augmentation in the list +# for aug in self.augmentations: +# transform = aug(aug_input) +# all_transforms.append(transform) +# img = aug_input.image +# +# # Combine all the transformations into a TransformList +# transforms = T.TransformList(all_transforms) +# +# # If there are annotations, apply the same transforms to them +# if "annotations" in dataset_dict: +# annos = dataset_dict.pop("annotations") +# for anno in annos: +# bbox = anno["bbox"] +# anno["bbox"] = transforms.apply_box(bbox) +# # Ensure the bounding box has the correct shape [N, 4] +# bbox = transforms.apply_box(np.array(bbox).reshape(1, 4)).squeeze(0) # Ensure shape [4] +# anno["bbox"] = bbox.tolist() # Convert back to list +# +# # Transform segmentation masks +# if "segmentation" in anno: +# segm = anno["segmentation"] +# if isinstance(segm, list): # Polygon format +# transformed_segm = [] +# for poly in segm: +# poly = np.array(poly).reshape(-1, 2) # Ensure shape (N, 2) +# transformed_poly = transforms.apply_polygons([poly])[0] # Apply and get the first element +# transformed_segm.append(transformed_poly.tolist()) +# anno["segmentation"] = transformed_segm +# else: +# raise ValueError("Unexpected segmentation format.") +# +# instances = utils.annotations_to_instances(annos, img.shape[:2]) +# dataset_dict["instances"] = utils.filter_empty_instances(instances) +# +# dataset_dict["image"] = torch.as_tensor(img.transpose(2, 0, 1).astype("float32")) +# +# return dataset_dict + +class FlexibleDatasetMapper(DatasetMapper): def __init__(self, cfg, is_train=True, augmentations=None): + if augmentations is None: + augmentations = [] + + # Wrap the augmentations list in AugmentationList inside the call to the superclass + super().__init__( + is_train=is_train, + augmentations=augmentations, + image_format=cfg.INPUT.FORMAT, + use_instance_mask=cfg.MODEL.MASK_ON, + use_keypoint=cfg.MODEL.KEYPOINT_ON, + instance_mask_format=cfg.INPUT.MASK_FORMAT, + keypoint_hflip_indices=None, + precomputed_proposal_topk=None, + recompute_boxes=False + ) self.cfg = cfg self.is_train = is_train - self.rgb_mapper = DatasetMapper(cfg, is_train=is_train) def __call__(self, dataset_dict): - # First, try to open the image using rasterio to determine the number of bands + if dataset_dict is None: + print("Received None for dataset_dict, skipping this entry.") + return None + try: + # If idx is passed with dataset_dict, capture it + #idx = dataset_dict.get("index", "unknown") + #print(f"Processing entry at index {idx}: {dataset_dict}") + # Handle multi-band image loading with rasterio.open(dataset_dict["file_name"]) as src: - num_bands = src.count - except rasterio.errors.RasterioIOError as e: - raise ValueError(f"Error opening image file: {dataset_dict['file_name']}") from e + img = src.read() + img = np.transpose(img, (1, 2, 0)).astype("float32") - # If the image has 3 bands, use the standard RGB DatasetMapper - if num_bands == 3: - return self.rgb_mapper(dataset_dict) - else: - # Otherwise, handle the image with the custom multi-band mapper - return self.multi_band_mapper(dataset_dict) + # If it's a 3-band image, use the inherited behavior + if img.shape[-1] == 3: + return super().__call__(dataset_dict) - def multi_band_mapper(self, dataset_dict): - dataset_dict = dataset_dict.copy() # Make a copy of the dataset dict - with rasterio.open(dataset_dict["file_name"]) as src: - img = src.read() # Read all bands - img = np.transpose(img, (1, 2, 0)) # Convert to HWC format + # Otherwise, handle custom multi-band logic + aug_input = T.AugInput(img) + transforms = self.augmentations(aug_input) # Apply the augmentations + img = aug_input.image - dataset_dict["image"] = torch.as_tensor(img.transpose(2, 0, 1).astype("float32")) - - # Ensure ground truth instances (annotations) are included - annos = dataset_dict.pop("annotations", []) - instances = utils.annotations_to_instances(annos, dataset_dict["image"].shape[1:]) - dataset_dict["instances"] = utils.filter_empty_instances(instances) - - return dataset_dict + dataset_dict["image"] = torch.as_tensor(img.transpose(2, 0, 1).astype("float32")) + if "annotations" in dataset_dict: + # Apply transforms to the annotations in the original dataset_dict + dataset_dict = super()._transform_annotations(dataset_dict, transforms, img.shape[:2]) + + return dataset_dict + + except Exception as e: + # Handle and log the error + if dataset_dict is not None: + file_name = dataset_dict.get('file_name', 'unknown') + print(f"Error processing {file_name}: {e}") + else: + print(f"Error processing an entry: {e}") + return None class LossEvalHook(HookBase): """Do inference and get the loss metric. @@ -222,7 +331,6 @@ def __init__(self, cfg, patience): # noqa: D107 super().__init__(cfg) #self.data_loader = build_train_loader(cfg) # Use custom data loader - def train(self): """Run training. @@ -305,7 +413,6 @@ def build_train_loader(cls, cfg): """ augmentations = [ T.RandomRotation(angle=[90, 90], expand=False), - T.RandomLighting(0.7), T.RandomFlip(prob=0.4, horizontal=True, vertical=False), T.RandomFlip(prob=0.4, horizontal=False, vertical=True), ] @@ -313,6 +420,7 @@ def build_train_loader(cls, cfg): # Some augmentations are only applicable to 3-band images if cfg.IMGMODE == "rgb": augmentations.append(T.RandomBrightness(0.8, 1.8), + T.RandomLighting(0.7), T.RandomContrast(0.6, 1.3), T.RandomSaturation(0.8, 1.4)) @@ -351,6 +459,10 @@ def build_train_loader(cls, cfg): augmentations=augmentations, ), ) + + @classmethod + def build_test_loader(cls, cfg, dataset_name): + return build_detection_test_loader(cfg, dataset_name, mapper=FlexibleDatasetMapper(cfg, is_train=False)) def get_tree_dicts(directory: str, classes: List[str] = None, classes_at: str = None) -> List[Dict]: """Get the tree dictionaries. @@ -413,8 +525,7 @@ def get_tree_dicts(directory: str, classes: List[str] = None, classes_at: str = "bbox": [np.min(px), np.min(py), np.max(px), np.max(py)], "bbox_mode": BoxMode.XYXY_ABS, "segmentation": [poly], - "category_id": classes.index(features["properties"][classes_at]), # id - # "category_id": 0, #id + "category_id": classes.index(features["properties"][classes_at]), "iscrowd": 0, } else: diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index bafd259a..7c20c235 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -240,23 +240,6 @@ def process_tile_ms( with rasterio.open(out_tif, "w", **out_meta) as dest: dest.write(out_img) - # Images withmore than 4 bands are not supported by cv2 - # Save all bands as an image if needed (not just the first 3 bands) - #band_images = [] - #for band_index in range(out_img.shape[0]): - # band_image = out_img[band_index, :, :] - # if np.max(band_image) > 255: - # band_image = 255 * band_image / np.max(band_image) - # band_images.append(band_image.astype(np.uint8)) - - # Stack the bands into a single image array - # Does the band order need to be reversed as in the RGB case? - #full_image = np.stack(band_images, axis=-1) - - # Save the full image with potentially more than 3 bands - #full_image_path = out_path_root.with_suffix(".png") - #cv2.imwrite(str(full_image_path.resolve()), full_image) - if overlapping_crowns is not None: return data, out_path_root, overlapping_crowns, minx, miny, buffer From 8b417e87547781da8c1fb0cca2cb6f3b762fe34f Mon Sep 17 00:00:00 2001 From: James Ball Date: Fri, 23 Aug 2024 12:40:02 +0100 Subject: [PATCH 25/63] ms training --- detectree2/models/train.py | 43 +++++++++++++++++++++++--------------- 1 file changed, 26 insertions(+), 17 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 3855c876..3a06f857 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -134,7 +134,7 @@ def __init__(self, cfg, is_train=True, augmentations=None): if augmentations is None: augmentations = [] - # Wrap the augmentations list in AugmentationList inside the call to the superclass + # Pass the raw list of augmentations (do not wrap it in T.AugmentationList here) super().__init__( is_train=is_train, augmentations=augmentations, @@ -148,21 +148,29 @@ def __init__(self, cfg, is_train=True, augmentations=None): ) self.cfg = cfg self.is_train = is_train + self.logger = logging.getLogger(__name__) + mode = "training" if is_train else "inference" + self.logger.info(f"[FlexibleDatasetMapper] Augmentations used in {mode}: {augmentations}") def __call__(self, dataset_dict): if dataset_dict is None: - print("Received None for dataset_dict, skipping this entry.") + self.logger.warning("Received None for dataset_dict, skipping this entry.") return None try: - # If idx is passed with dataset_dict, capture it - #idx = dataset_dict.get("index", "unknown") - #print(f"Processing entry at index {idx}: {dataset_dict}") # Handle multi-band image loading with rasterio.open(dataset_dict["file_name"]) as src: img = src.read() + if img is None: + raise ValueError(f"Image data is None for file: {dataset_dict['file_name']}") img = np.transpose(img, (1, 2, 0)).astype("float32") + # Size check similar to utils.check_image_size + if img.shape[:2] != (dataset_dict.get("height"), dataset_dict.get("width")): + self.logger.warning( + f"Image size {img.shape[:2]} does not match expected size {(dataset_dict.get('height'), dataset_dict.get('width'))}." + ) + # If it's a 3-band image, use the inherited behavior if img.shape[-1] == 3: return super().__call__(dataset_dict) @@ -172,21 +180,22 @@ def __call__(self, dataset_dict): transforms = self.augmentations(aug_input) # Apply the augmentations img = aug_input.image - dataset_dict["image"] = torch.as_tensor(img.transpose(2, 0, 1).astype("float32")) + dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(img.transpose(2, 0, 1))) + + # Handle semantic segmentation if present + if "sem_seg_file_name" in dataset_dict: + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) if "annotations" in dataset_dict: # Apply transforms to the annotations in the original dataset_dict - dataset_dict = super()._transform_annotations(dataset_dict, transforms, img.shape[:2]) + self._transform_annotations(dataset_dict, transforms, img.shape[:2]) return dataset_dict - + except Exception as e: - # Handle and log the error - if dataset_dict is not None: - file_name = dataset_dict.get('file_name', 'unknown') - print(f"Error processing {file_name}: {e}") - else: - print(f"Error processing an entry: {e}") + file_name = dataset_dict.get('file_name', 'unknown') if dataset_dict else 'unknown' + self.logger.error(f"Error processing {file_name}: {e}") return None class LossEvalHook(HookBase): @@ -419,12 +428,12 @@ def build_train_loader(cls, cfg): # Some augmentations are only applicable to 3-band images if cfg.IMGMODE == "rgb": - augmentations.append(T.RandomBrightness(0.8, 1.8), + augmentations.extend(T.RandomBrightness(0.8, 1.8), T.RandomLighting(0.7), T.RandomContrast(0.6, 1.3), T.RandomSaturation(0.8, 1.4)) - if cfg.RESIZE: + if cfg.RESIZE == "fixed": augmentations.append(T.Resize((1000, 1000))) elif cfg.RESIZE == "random": size = None @@ -684,7 +693,7 @@ def setup_cfg( num_classes=1, eval_period=100, out_dir="./train_outputs", - resize=True, + resize="fixed", # fixed or random imgmode="rgb", ): """Set up config object # noqa: D417. From f02fdb05a7c6f1937d5ae874465f47be8f516499 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 26 Aug 2024 18:39:10 +0100 Subject: [PATCH 26/63] inhertited processes --- detectree2/models/train.py | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 3a06f857..9e10480d 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -183,15 +183,16 @@ def __call__(self, dataset_dict): dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(img.transpose(2, 0, 1))) # Handle semantic segmentation if present - if "sem_seg_file_name" in dataset_dict: - sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) - dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + # THIS CAN BE HANDLED BY INHERITING FROM DatasetMapper? + #if "sem_seg_file_name" in dataset_dict: + # sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + # dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) - if "annotations" in dataset_dict: - # Apply transforms to the annotations in the original dataset_dict - self._transform_annotations(dataset_dict, transforms, img.shape[:2]) + #if "annotations" in dataset_dict: + # # Apply transforms to the annotations in the original dataset_dict + # self._transform_annotations(dataset_dict, transforms, img.shape[:2]) - return dataset_dict + return super().__call__(dataset_dict) except Exception as e: file_name = dataset_dict.get('file_name', 'unknown') if dataset_dict else 'unknown' @@ -428,10 +429,12 @@ def build_train_loader(cls, cfg): # Some augmentations are only applicable to 3-band images if cfg.IMGMODE == "rgb": - augmentations.extend(T.RandomBrightness(0.8, 1.8), - T.RandomLighting(0.7), - T.RandomContrast(0.6, 1.3), - T.RandomSaturation(0.8, 1.4)) + augmentations.extend([ + T.RandomBrightness(0.8, 1.8), + T.RandomLighting(0.7), + T.RandomContrast(0.6, 1.3), + T.RandomSaturation(0.8, 1.4) + ]) if cfg.RESIZE == "fixed": augmentations.append(T.Resize((1000, 1000))) From 925c3de51995f147cdf2a317dcf3cf57f66e6d5e Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 26 Aug 2024 19:19:05 +0100 Subject: [PATCH 27/63] inhertited processes --- detectree2/models/train.py | 26 ++++++++++++++++---------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 9e10480d..a8088135 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -183,16 +183,22 @@ def __call__(self, dataset_dict): dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(img.transpose(2, 0, 1))) # Handle semantic segmentation if present - # THIS CAN BE HANDLED BY INHERITING FROM DatasetMapper? - #if "sem_seg_file_name" in dataset_dict: - # sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) - # dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) - - #if "annotations" in dataset_dict: - # # Apply transforms to the annotations in the original dataset_dict - # self._transform_annotations(dataset_dict, transforms, img.shape[:2]) - - return super().__call__(dataset_dict) + # Can this be handled by inheriting from DatasetMapper? + if "sem_seg_file_name" in dataset_dict: + sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) + dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) + + if not self.is_train: + # USER: Modify this if you want to keep them for some reason. + dataset_dict.pop("annotations", None) + dataset_dict.pop("sem_seg_file_name", None) + return dataset_dict + + if "annotations" in dataset_dict: + # Apply transforms to the annotations in the original dataset_dict + self._transform_annotations(dataset_dict, transforms, img.shape[:2]) + + return dataset_dict except Exception as e: file_name = dataset_dict.get('file_name', 'unknown') if dataset_dict else 'unknown' From 5027a49ee651831cb03a323ee0860b17495ff532 Mon Sep 17 00:00:00 2001 From: James Ball Date: Wed, 28 Aug 2024 15:59:58 +0100 Subject: [PATCH 28/63] augmentations scaling --- detectree2/models/train.py | 35 +++++++++++++++++++++++++++++------ 1 file changed, 29 insertions(+), 6 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index a8088135..2ce98459 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -399,9 +399,27 @@ def build_evaluator(cls, cfg, dataset_name, output_folder=None): def build_hooks(self): hooks = super().build_hooks() - # augmentations = [T.ResizeShortestEdge(short_edge_length=(1000, 1000), - # max_size=1333, - # sample_style='choice')] + if cfg.RESIZE == "random": + size = None + for i, datas in enumerate(DatasetCatalog.get(cfg.DATASETS.TRAIN[0])): + location = datas['file_name'] + try: + # Try to read with cv2 (for RGB images) + img = cv2.imread(location) + if img is not None: + size = img.shape[0] + else: + # Fall back to rasterio for multi-band images + with rasterio.open(location) as src: + size = src.height # Assuming square images + except Exception as e: + # Handle any errors that occur during loading + print(f"Error loading image {location}: {e}") + continue + break + augmentations = [T.ResizeShortestEdge([size, size], size+300)] + else: + augmentations = [T.ResizeShortestEdge([1000, 1000], 1333)] hooks.insert( -1, LossEvalHook( @@ -410,7 +428,7 @@ def build_hooks(self): build_detection_test_loader( self.cfg, self.cfg.DATASETS.TEST, - FlexibleDatasetMapper(self.cfg, True) # Need to edit this for custom dataset + FlexibleDatasetMapper(self.cfg, True, augmentations=augmentations) ), self.patience, ), @@ -436,14 +454,14 @@ def build_train_loader(cls, cfg): # Some augmentations are only applicable to 3-band images if cfg.IMGMODE == "rgb": augmentations.extend([ - T.RandomBrightness(0.8, 1.8), + T.RandomBrightness(0.7, 1.5), T.RandomLighting(0.7), T.RandomContrast(0.6, 1.3), T.RandomSaturation(0.8, 1.4) ]) if cfg.RESIZE == "fixed": - augmentations.append(T.Resize((1000, 1000))) + augmentations.append(T.ResizeShortestEdge([1000, 1000], 1333)) elif cfg.RESIZE == "random": size = None for i, datas in enumerate(DatasetCatalog.get(cfg.DATASETS.TRAIN[0])): @@ -465,9 +483,14 @@ def build_train_loader(cls, cfg): if size: print("ADD RANDOM RESIZE WITH SIZE = ", size) + #min_size = int(size * 0.6) + #max_size = int(size * 1.4) + #augmentations.append(T.RandomResize(min_size=(min_size, min_size), max_size=max_size)) augmentations.append(T.ResizeScale(0.6, 1.4, size, size)) else: raise ValueError("Failed to determine image size for random resize") + elif cfg.RESIZE == "rand_fixed": + augmentations.append(T.ResizeScale(0.6, 1.4, 1000, 1000)) return build_detection_train_loader( cfg, From 065ad16a3d2473d25e7289ba84192ecb00d2d495 Mon Sep 17 00:00:00 2001 From: James Ball Date: Wed, 28 Aug 2024 16:31:20 +0100 Subject: [PATCH 29/63] augmentations scaling --- detectree2/models/train.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 2ce98459..c9e712b3 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -399,9 +399,9 @@ def build_evaluator(cls, cfg, dataset_name, output_folder=None): def build_hooks(self): hooks = super().build_hooks() - if cfg.RESIZE == "random": + if self.cfg.RESIZE == "random": size = None - for i, datas in enumerate(DatasetCatalog.get(cfg.DATASETS.TRAIN[0])): + for i, datas in enumerate(DatasetCatalog.get(self.cfg.DATASETS.TRAIN[0])): location = datas['file_name'] try: # Try to read with cv2 (for RGB images) From 5682e7581cc651202dac77f81280b70ae82523a4 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 2 Sep 2024 17:48:41 +0100 Subject: [PATCH 30/63] ms functions --- detectree2/models/train.py | 441 +++++++++++++++++++---------- detectree2/preprocessing/tiling.py | 4 +- 2 files changed, 298 insertions(+), 147 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index c9e712b3..d4e1a82e 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -9,6 +9,7 @@ import logging import os import random +import re import time from pathlib import Path from typing import Any, Dict, List @@ -19,6 +20,7 @@ import numpy as np import rasterio import torch +import torch.nn as nn from detectron2 import model_zoo from detectron2.checkpoint import DetectionCheckpointer # noqa:F401 from detectron2.config import get_cfg @@ -40,101 +42,31 @@ from detectron2.utils.logger import log_every_n_seconds from detectron2.utils.visualizer import ColorMode, Visualizer -# from IPython.display import display -# from PIL import Image - -#class FlexibleDatasetMapper: -# def __init__(self, cfg, is_train=True, augmentations=None): -# self.cfg = cfg -# self.is_train = is_train -# # Ensure augmentations is always a list -# if augmentations is None: -# augmentations = [] -# self.augmentations = augmentations -# self.rgb_mapper = DatasetMapper(cfg, is_train=is_train, augmentations=augmentations) -# -# def __call__(self, dataset_dict): -# # First, try to open the image using rasterio to determine the number of bands -# try: -# with rasterio.open(dataset_dict["file_name"]) as src: -# num_bands = src.count -# except rasterio.errors.RasterioIOError as e: -# raise ValueError(f"Error opening image file: {dataset_dict['file_name']}") from e -# -# # If the image has 3 bands, use the standard RGB DatasetMapper -# if num_bands == 3: -# print("Using RGB mapper") -# return self.rgb_mapper(dataset_dict) -# else: -# # Otherwise, handle the image with the custom multi-band mapper -# print("Using multi-band mapper") -# return self.multi_band_mapper(dataset_dict) -# -# def multi_band_mapper(self, dataset_dict): -# dataset_dict = dataset_dict.copy() # Make a copy of the dataset dict -# -# # Load the image using rasterio -# with rasterio.open(dataset_dict["file_name"]) as src: -# img = src.read() # Read all bands -# img = np.transpose(img, (1, 2, 0)) # Convert to HWC format (Height, Width, Channels) -# -# # Convert the image to float32 -# img = img.astype("float32") -# -# # Normalize the image if necessary (optional step) -# # Example: normalize to [0, 1] if the original image has high dynamic range values -# img /= 65535.0 # Assuming the original range is [0, 65535] for 16-bit images -# -# # Apply augmentations if provided -# if self.augmentations: -# aug_input = T.AugInput(img) -# all_transforms = [] -# -# # Apply each augmentation in the list -# for aug in self.augmentations: -# transform = aug(aug_input) -# all_transforms.append(transform) -# img = aug_input.image -# -# # Combine all the transformations into a TransformList -# transforms = T.TransformList(all_transforms) -# -# # If there are annotations, apply the same transforms to them -# if "annotations" in dataset_dict: -# annos = dataset_dict.pop("annotations") -# for anno in annos: -# bbox = anno["bbox"] -# anno["bbox"] = transforms.apply_box(bbox) -# # Ensure the bounding box has the correct shape [N, 4] -# bbox = transforms.apply_box(np.array(bbox).reshape(1, 4)).squeeze(0) # Ensure shape [4] -# anno["bbox"] = bbox.tolist() # Convert back to list -# -# # Transform segmentation masks -# if "segmentation" in anno: -# segm = anno["segmentation"] -# if isinstance(segm, list): # Polygon format -# transformed_segm = [] -# for poly in segm: -# poly = np.array(poly).reshape(-1, 2) # Ensure shape (N, 2) -# transformed_poly = transforms.apply_polygons([poly])[0] # Apply and get the first element -# transformed_segm.append(transformed_poly.tolist()) -# anno["segmentation"] = transformed_segm -# else: -# raise ValueError("Unexpected segmentation format.") -# -# instances = utils.annotations_to_instances(annos, img.shape[:2]) -# dataset_dict["instances"] = utils.filter_empty_instances(instances) -# -# dataset_dict["image"] = torch.as_tensor(img.transpose(2, 0, 1).astype("float32")) -# -# return dataset_dict class FlexibleDatasetMapper(DatasetMapper): + """ + A flexible dataset mapper that extends the standard DatasetMapper to handle multi-band images + and custom augmentations. + + This class is designed to work with datasets that may contain images with more than three channels + (e.g., multispectral images) and allows for custom augmentations to be applied. It also handles + semantic segmentation data if provided in the dataset. + + Args: + cfg (CfgNode): Configuration object containing dataset and model configurations. + is_train (bool): Flag indicating whether the mapper is being used for training. Default is True. + augmentations (list, optional): List of augmentations to be applied. Default is an empty list. + + Attributes: + cfg (CfgNode): Stores the configuration object for later use. + is_train (bool): Indicates whether the mapper is in training mode. + logger (Logger): Logger instance for logging messages. + """ def __init__(self, cfg, is_train=True, augmentations=None): if augmentations is None: augmentations = [] - # Pass the raw list of augmentations (do not wrap it in T.AugmentationList here) + # Initialize the base DatasetMapper class with provided parameters super().__init__( is_train=is_train, augmentations=augmentations, @@ -153,16 +85,27 @@ def __init__(self, cfg, is_train=True, augmentations=None): self.logger.info(f"[FlexibleDatasetMapper] Augmentations used in {mode}: {augmentations}") def __call__(self, dataset_dict): + """ + Process a single dataset dictionary, applying the necessary transformations and augmentations. + + Args: + dataset_dict (dict): A dictionary containing data for a single dataset item, including + file names and metadata. + + Returns: + dict: The processed dataset dictionary, or None if there was an error. + """ if dataset_dict is None: self.logger.warning("Received None for dataset_dict, skipping this entry.") return None try: - # Handle multi-band image loading + # Handle multi-band image loading using rasterio with rasterio.open(dataset_dict["file_name"]) as src: img = src.read() if img is None: raise ValueError(f"Image data is None for file: {dataset_dict['file_name']}") + # Transpose image dimensions to match expected format (H, W, C) img = np.transpose(img, (1, 2, 0)).astype("float32") # Size check similar to utils.check_image_size @@ -171,7 +114,7 @@ def __call__(self, dataset_dict): f"Image size {img.shape[:2]} does not match expected size {(dataset_dict.get('height'), dataset_dict.get('width'))}." ) - # If it's a 3-band image, use the inherited behavior + # If it's a 3-band image, delegate processing to the parent class if img.shape[-1] == 3: return super().__call__(dataset_dict) @@ -183,19 +126,18 @@ def __call__(self, dataset_dict): dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(img.transpose(2, 0, 1))) # Handle semantic segmentation if present - # Can this be handled by inheriting from DatasetMapper? if "sem_seg_file_name" in dataset_dict: sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) if not self.is_train: - # USER: Modify this if you want to keep them for some reason. + # If not in training mode, remove annotations and segmentation file names dataset_dict.pop("annotations", None) dataset_dict.pop("sem_seg_file_name", None) return dataset_dict if "annotations" in dataset_dict: - # Apply transforms to the annotations in the original dataset_dict + # Apply the transformations to the annotations self._transform_annotations(dataset_dict, transforms, img.shape[:2]) return dataset_dict @@ -206,24 +148,34 @@ def __call__(self, dataset_dict): return None class LossEvalHook(HookBase): - """Do inference and get the loss metric. + """ + A custom hook for evaluating loss during training and managing model checkpoints based on evaluation metrics. - Class to: - - Do inference of dataset like an Evaluator does - - Get the loss metric like the trainer does - https://github.com/facebookresearch/detectron2/blob/master/detectron2/evaluation/evaluator.py - https://github.com/facebookresearch/detectron2/blob/master/detectron2/engine/train_loop.py - See https://gist.github.com/ortegatron/c0dad15e49c2b74de8bb09a5615d9f6b + This hook is designed to: + - Perform inference on a dataset similarly to an Evaluator. + - Calculate and log the loss metric during training. + - Save the best model checkpoint based on a specified evaluation metric (e.g., AP50). + - Implement early stopping if the evaluation metric does not improve over a specified number of evaluations. Attributes: - model: model to train - period: number of iterations between evaluations - data_loader: data loader to use for evaluation - patience: number of evaluation periods to wait for improvement + _model: The model to evaluate. + _period: Number of iterations between evaluations. + _data_loader: The data loader used for evaluation. + patience: Number of evaluation periods to wait before early stopping. + iter: Tracks the number of evaluations since the last improvement in the evaluation metric. + max_ap: The best evaluation metric (e.g., AP50) achieved during training. + best_iter: The iteration at which the best evaluation metric was achieved. """ - def __init__(self, eval_period, model, data_loader, patience): - """Inits LossEvalHook.""" + """ + Initialize the LossEvalHook. + + Args: + eval_period (int): The number of iterations between evaluations. + model (torch.nn.Module): The model to evaluate. + data_loader (torch.utils.data.DataLoader): The data loader for evaluation. + patience (int): The number of evaluation periods to wait for improvement before early stopping. + """ self._model = model self._period = eval_period self._data_loader = data_loader @@ -233,10 +185,14 @@ def __init__(self, eval_period, model, data_loader, patience): self.best_iter = 0 def _do_loss_eval(self): - """Copying inference_on_dataset from evaluator.py. + """ + Perform inference on the dataset and calculate the average loss. + + This method is adapted from `inference_on_dataset` in Detectron2's evaluator. + It also calculates and logs the AP50 metric and updates the best model checkpoint if needed. Returns: - _type_: _description_ + list: A list of loss values for each batch in the dataset. """ total = len(self._data_loader) num_warmup = min(5, total - 1) @@ -246,6 +202,7 @@ def _do_loss_eval(self): losses = [] for idx, inputs in enumerate(self._data_loader): if idx == num_warmup: + # Reset the start time after the warm-up phase start_time = time.perf_counter() total_compute_time = 0 start_compute_time = time.perf_counter() @@ -255,6 +212,7 @@ def _do_loss_eval(self): iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup) seconds_per_img = total_compute_time / iters_after_start if idx >= num_warmup * 2 or seconds_per_img > 5: + # Log progress and estimated time remaining total_seconds_per_img = (time.perf_counter() - start_time) / iters_after_start eta = datetime.timedelta(seconds=int(total_seconds_per_img * (total - idx - 1))) log_every_n_seconds( @@ -263,11 +221,13 @@ def _do_loss_eval(self): str(eta)), n=5, ) + # Calculate loss for the current batch loss_batch = self._get_loss(inputs) losses.append(loss_batch) + mean_loss = np.mean(losses) - # print(self.trainer.cfg.DATASETS.TEST) - # Combine the AP50s of the different datasets + + # Calculate the average AP50 across datasets if multiple datasets are used for testing if len(self.trainer.cfg.DATASETS.TEST) > 1: APs = [] for dataset in self.trainer.cfg.DATASETS.TEST: @@ -275,7 +235,10 @@ def _do_loss_eval(self): AP = sum(APs) / len(APs) else: AP = self.trainer.test(self.trainer.cfg, self.trainer.model)["segm"]["AP50"] - print("Av. AP50 =", AP) + + print("Av. segm AP50 =", AP) + + # Store the calculated loss and AP50 in the trainer's storage self.trainer.APs.append(AP) self.trainer.storage.put_scalar("validation_loss", mean_loss) self.trainer.storage.put_scalar("validation_ap", AP) @@ -284,15 +247,17 @@ def _do_loss_eval(self): return losses def _get_loss(self, data): - """Calculate loss in train_loop. + """ + Compute the loss for a given batch of data. Args: - data (_type_): _description_ + data (dict): A batch of input data. Returns: - _type_: _description_ + float: The total loss for the batch. """ metrics_dict = self._model(data) + # Detach and move to CPU for logging metrics_dict = { k: v.detach().cpu().item() if isinstance(v, torch.Tensor) else float(v) for k, v in metrics_dict.items() @@ -301,60 +266,74 @@ def _get_loss(self, data): return total_losses_reduced def after_step(self): + """ + Hook to be called after each training iteration to evaluate the model and manage checkpoints. + + - Evaluates the model at regular intervals. + - Saves the best model checkpoint based on the AP50 metric. + - Implements early stopping if the AP50 does not improve after a set number of evaluations. + """ next_iter = self.trainer.iter + 1 is_final = next_iter == self.trainer.max_iter if is_final or (self._period > 0 and next_iter % self._period == 0): self._do_loss_eval() + # Check if the current AP50 is the best so far if self.max_ap < self.trainer.APs[-1]: self.iter = 0 self.max_ap = self.trainer.APs[-1] + # Save the current best model self.trainer.checkpointer.save("model_" + str(len(self.trainer.APs))) self.best_iter = self.trainer.iter else: self.iter += 1 if self.iter == self.patience: + # Early stopping condition met self.trainer.early_stop = True print("Early stopping occurs in iter {}, max ap is {}".format(self.best_iter, self.max_ap)) self.trainer.storage.put_scalars(timetest=12) def after_train(self): + """ + Hook to be called after training is complete to load the best model checkpoint based on AP50. + + - Selects and loads the model checkpoint with the best AP50. + """ if not self.trainer.APs: print("No APs were recorded during training. Skipping model selection.") return # Select the model with the best AP50 index = self.trainer.APs.index(max(self.trainer.APs)) + 1 - # Error in demo: - # AssertionError: Checkpoint /__w/detectree2/detectree2/detectree2-data/paracou-out/train_outputs-1/model_1.pth - # not found! - # Therefore sleep is attempt to allow CI to pass, but it often still fails. + # Error handling for checkpoint loading, with a sleep to ensure file availability in CI environments time.sleep(15) self.trainer.checkpointer.load(self.trainer.cfg.OUTPUT_DIR + '/model_' + str(index) + '.pth') # See https://jss367.github.io/data-augmentation-in-detectron2.html for data augmentation advice class MyTrainer(DefaultTrainer): - """Summary. + """ + Custom Trainer class that extends the DefaultTrainer. - Args: - DefaultTrainer (_type_): _description_ + This trainer adds flexibility for handling different image types (e.g., RGB and multi-band images) + and custom training behavior, such as early stopping and specialized data augmentation strategies. - Returns: - _type_: _description_ + Args: + cfg (CfgNode): Configuration object containing the model and dataset configurations. + patience (int): Number of evaluation periods to wait for improvement before early stopping. """ def __init__(self, cfg, patience): # noqa: D107 self.patience = patience super().__init__(cfg) - #self.data_loader = build_train_loader(cfg) # Use custom data loader def train(self): - """Run training. + """ + Run the training loop. - Args: - start_iter, max_iter (int): See docs above + This method overrides the DefaultTrainer's train method to include early stopping and + custom logging of Average Precision (AP) metrics. Returns: - OrderedDict of results, if evaluation is enabled. Otherwise None. + OrderedDict: Results from evaluation, if evaluation is enabled. Otherwise, None. """ start_iter = self.start_iter @@ -385,6 +364,7 @@ def train(self): raise finally: self.after_train() + # Verify the results if testing is enabled and this is the main process if len(self.cfg.TEST.EXPECTED_RESULTS) and comm.is_main_process(): assert hasattr(self, "_last_eval_results"), "No evaluation results obtained during training!" verify_results(self.cfg, self._last_eval_results) @@ -392,19 +372,42 @@ def train(self): @classmethod def build_evaluator(cls, cfg, dataset_name, output_folder=None): + """ + Build the evaluator for the model. + + Args: + cfg (CfgNode): Configuration object. + dataset_name (str): Name of the dataset to evaluate. + output_folder (str, optional): Directory to save evaluation results. Defaults to "eval". + + Returns: + COCOEvaluator: An evaluator for COCO-style datasets. + """ if output_folder is None: os.makedirs("eval", exist_ok=True) output_folder = "eval" return COCOEvaluator(dataset_name, cfg, True, output_folder) def build_hooks(self): + """ + Build the training hooks, including the custom LossEvalHook. + + This method adds a custom hook for evaluating the model's loss during training, with support for + early stopping based on the AP50 metric. + + Returns: + list: A list of hooks to be used during training. + """ hooks = super().build_hooks() + + # Determine the appropriate resize strategy based on the configuration if self.cfg.RESIZE == "random": size = None + # Attempt to determine the image size from the training dataset for i, datas in enumerate(DatasetCatalog.get(self.cfg.DATASETS.TRAIN[0])): location = datas['file_name'] try: - # Try to read with cv2 (for RGB images) + # Attempt to read the image with OpenCV (for RGB images) img = cv2.imread(location) if img is not None: size = img.shape[0] @@ -417,9 +420,13 @@ def build_hooks(self): print(f"Error loading image {location}: {e}") continue break + # Define augmentation based on the determined size augmentations = [T.ResizeShortestEdge([size, size], size+300)] else: + # Use fixed size resizing as a default augmentations = [T.ResizeShortestEdge([1000, 1000], 1333)] + + # Insert the custom LossEvalHook before the last hook (typically the evaluation hook) hooks.insert( -1, LossEvalHook( @@ -437,21 +444,27 @@ def build_hooks(self): @classmethod def build_train_loader(cls, cfg): - """Build the train loader with flexibility for RGB and multi-band images. + """ + Build the training data loader with support for custom augmentations and image types. + + This method configures the data loader to apply specific augmentations depending on the image mode + (RGB or multi-band) and resize strategy defined in the configuration. Args: - cfg (_type_): _description_ + cfg (CfgNode): Configuration object. Returns: - _type_: _description_ + DataLoader: A data loader for the training dataset. """ + + # Define basic augmentations including rotation and flipping augmentations = [ T.RandomRotation(angle=[90, 90], expand=False), T.RandomFlip(prob=0.4, horizontal=True, vertical=False), T.RandomFlip(prob=0.4, horizontal=False, vertical=True), ] - # Some augmentations are only applicable to 3-band images + # Additional augmentations for RGB images if cfg.IMGMODE == "rgb": augmentations.extend([ T.RandomBrightness(0.7, 1.5), @@ -460,6 +473,7 @@ def build_train_loader(cls, cfg): T.RandomSaturation(0.8, 1.4) ]) + # Add resizing augmentations based on the resize strategy if cfg.RESIZE == "fixed": augmentations.append(T.ResizeShortestEdge([1000, 1000], 1333)) elif cfg.RESIZE == "random": @@ -503,6 +517,19 @@ def build_train_loader(cls, cfg): @classmethod def build_test_loader(cls, cfg, dataset_name): + """ + Build the test data loader. + + This method configures the data loader for evaluation, using the FlexibleDatasetMapper + to handle custom augmentations and image types. + + Args: + cfg (CfgNode): Configuration object. + dataset_name (str): Name of the dataset to load for testing. + + Returns: + DataLoader: A data loader for the test dataset. + """ return build_detection_test_loader(cfg, dataset_name, mapper=FlexibleDatasetMapper(cfg, is_train=False)) def get_tree_dicts(directory: str, classes: List[str] = None, classes_at: str = None) -> List[Dict]: @@ -590,24 +617,42 @@ def combine_dicts(root_dir: str, mode: str = "train", classes: List[str] = None, classes_at: str = None) -> List[Dict]: - """Join tree dicts from different directories. + """ + Combine dictionaries from different directories based on the specified mode. + + This function aggregates tree dictionaries from multiple directories within a root directory. + Depending on the mode, it either combines dictionaries from all directories, + all except a specified validation directory, or only from the validation directory. Args: - root_dir: - val_dir: + root_dir (str): The root directory containing subdirectories with tree dictionaries. + val_dir (int): The index (1-based) of the validation directory to exclude or use depending on the mode. + mode (str, optional): The mode of operation. Can be "train", "val", or "full". + "train" excludes the validation directory, + "val" includes only the validation directory, + and "full" includes all directories. Defaults to "train". + classes (List[str], optional): A list of classes to filter the dictionaries by. Defaults to None. + classes_at (str, optional): A key to specify where the classes are located within the dictionary structure. Defaults to None. Returns: - Concatenated array of dictionaries over all directories + List[Dict]: A list of combined dictionaries from the specified directories. """ + # Get a list of all directories within the root directory train_dirs = [os.path.join(root_dir, dir) for dir in os.listdir(root_dir)] + + # Handle the different modes for combining dictionaries if mode == "train": + # Exclude the validation directory from the list of directories del train_dirs[(val_dir - 1)] tree_dicts = [] for d in train_dirs: + # Combine dictionaries from all directories except the validation directory tree_dicts += get_tree_dicts(d, classes=classes, classes_at=classes_at) elif mode == "val": + # Use only the validation directory tree_dicts = get_tree_dicts(train_dirs[(val_dir - 1)], classes=classes, classes_at=classes_at) elif mode == "full": + # Combine dictionaries from all directories, including the validation directory tree_dicts = [] for d in train_dirs: tree_dicts += get_tree_dicts(d, classes=classes, classes_at=classes_at) @@ -645,6 +690,8 @@ def register_train_data(train_location, name: string to name data val_fold: fold assigned for validation and tuning. If not given, will take place on all folds. + classes: list of classes to include + classes_at: column name for classes """ if val_fold is not None: for d in ["train", "val"]: @@ -665,8 +712,15 @@ def register_train_data(train_location, MetadataCatalog.get(name + "_" + "full").set(thing_classes=classes) -def read_data(out_dir): - """Function that will read the classes that are recorded during tiling.""" +def get_classes(out_dir): + """Function that will read the classes that are recorded during tiling. + + Args: + out_dir: directory where classes.txt is located + + Returns: + list of classes + """ list = [] classes_txt = out_dir + 'classes.txt' # open file and read the content in a list @@ -692,14 +746,23 @@ def remove_registered_data(name="tree"): def register_test_data(test_location, name="tree"): - """Register data for testing.""" + """Register data for testing. + + Args: + test_location: directory containing test data + name: string to name data + """ d = "test" DatasetCatalog.register(name + "_" + d, lambda d=d: get_tree_dicts(test_location)) MetadataCatalog.get(name + "_" + d).set(thing_classes=["tree"]) def load_json_arr(json_path): - """Load json array.""" + """Load json array. + + Args: + json_path: path to json file + """ lines = [] with open(json_path, "r") as f: for line in f: @@ -725,8 +788,9 @@ def setup_cfg( num_classes=1, eval_period=100, out_dir="./train_outputs", - resize="fixed", # fixed or random + resize="fixed", # fixed or random or rand_fixed imgmode="rgb", + num_bands=3, ): """Set up config object # noqa: D417. @@ -749,6 +813,11 @@ def setup_cfg( eval_period: number of iterations between evaluations out_dir: directory to save outputs """ + + # Validate the resize parameter + if resize not in {"fixed", "random", "rand_fixed"}: + raise ValueError(f"Invalid resize option '{resize}'. Must be 'fixed', 'random', or 'rand_fixed'.") + cfg = get_cfg() cfg.merge_from_file(model_zoo.get_config_file(base_model)) cfg.DATASETS.TRAIN = trains @@ -776,7 +845,16 @@ def setup_cfg( cfg.TEST.EVAL_PERIOD = eval_period cfg.RESIZE = resize cfg.INPUT.MIN_SIZE_TRAIN = 1000 - cfg.IMGMODE = imgmode + cfg.IMGMODE = imgmode # rgb or multispectral + if num_bands > 3: + # Adjust PIXEL_MEAN and PIXEL_STD for the number of bands + default_pixel_mean = cfg.MODEL.PIXEL_MEAN + default_pixel_std = cfg.MODEL.PIXEL_STD + # Extend or truncate the PIXEL_MEAN and PIXEL_STD based on num_bands + cfg.MODEL.PIXEL_MEAN = (default_pixel_mean * (num_bands // len(default_pixel_mean)) + + default_pixel_mean[:num_bands % len(default_pixel_mean)]) + cfg.MODEL.PIXEL_STD = (default_pixel_std * (num_bands // len(default_pixel_std)) + + default_pixel_std[:num_bands % len(default_pixel_std)]) return cfg @@ -787,7 +865,17 @@ def predictions_on_data(directory=None, scale=1, geos_exist=True, num_predictions=0): - """Prediction produced from a test folder and outputted to predictions folder.""" + """Prediction produced from a test folder and outputted to predictions folder. + + Args: + directory: directory containing test data + predictor: predictor object + trees_metadata: metadata for trees + save: boolean to save predictions + scale: scale of image + geos_exist: boolean to determine if geojson files exist + num_predictions: number of predictions to make + """ test_location = directory + "/test" pred_dir = test_location + "/predictions" @@ -833,6 +921,69 @@ def predictions_on_data(directory=None, with open(output_file, "w") as dest: json.dump(evaluations, dest) +def modify_conv1_weights(model, num_input_channels): + """ + Modify the weights of the first convolutional layer (conv1) to accommodate a different number of input channels. + + This function adjusts the weights of the `conv1` layer in the model's backbone to support a custom number + of input channels. It creates a new weight tensor with the desired number of input channels, + and initializes it by repeating the weights of the original channels. + + Args: + model (torch.nn.Module): The model containing the convolutional layer to modify. + num_input_channels (int): The number of input channels for the new conv1 layer. + + """ + with torch.no_grad(): + # Retrieve the original weights of the conv1 layer + old_weights = model.backbone.bottom_up.stem.conv1.weight + + # Create a new weight tensor with the desired number of input channels + # The shape is (out_channels, in_channels, height, width) + new_weights = torch.zeros((old_weights.size(0), num_input_channels, *old_weights.shape[2:])) + + # Initialize the new weights by repeating the original weights across the new channels + # This example repeats the first 3 channels if num_input_channels > 3 + for i in range(num_input_channels): + new_weights[:, i, :, :] = old_weights[:, i % 3, :, :] + + # Create a new conv1 layer with the updated number of input channels + model.backbone.bottom_up.stem.conv1 = nn.Conv2d( + num_input_channels, old_weights.size(0), kernel_size=7, stride=2, padding=3, bias=False + ) + + # Copy the modified weights into the new conv1 layer + model.backbone.bottom_up.stem.conv1.weight.copy_(new_weights) + + +def get_latest_model_path(output_dir: str) -> str: + """ + Find the model file with the highest index in the specified output directory. + + Args: + output_dir (str): The directory where the model files are stored. + + Returns: + str: The path to the model file with the highest index. + """ + # Regular expression to match model files with the pattern "model_X.pth" + model_pattern = re.compile(r"model_(\d+)\.pth") + + # List all files in the output directory + files = os.listdir(output_dir) + + # Find all files that match the pattern and extract their indices + model_files = [(f, int(model_pattern.search(f).group(1))) for f in files if model_pattern.search(f)] + + if not model_files: + raise FileNotFoundError(f"No model files found in the directory {output_dir}") + + # Sort the files by index in descending order and select the highest one + latest_model_file = max(model_files, key=lambda x: x[1])[0] + + # Return the full path to the latest model file + return os.path.join(output_dir, latest_model_file) + if __name__ == "__main__": train_location = "/content/drive/Shareddrives/detectree2/data/Paracou/tiles/train/" diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 7c20c235..3f9df300 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -427,10 +427,10 @@ def is_overlapping_box(test_boxes_array, train_box): return False -def record_data(crowns, +def record_classes(crowns, out_dir, column='status'): - """Function that will record a list of classes into a file that can be readed during training. + """Function that will record a list of classes into a file that can be read during training. Args: crowns: gpd dataframe with the crowns From b5f621625d1bb50bb90c2b440def8fb983237fc8 Mon Sep 17 00:00:00 2001 From: James Ball Date: Tue, 3 Sep 2024 18:24:09 +0100 Subject: [PATCH 31/63] tutorial updates --- README.md | 4 +- docs/source/tutorial.rst | 260 +++++++++++++++++++++++++++--- report/figures/train_val_loss.png | Bin 0 -> 44808 bytes 3 files changed, 242 insertions(+), 22 deletions(-) create mode 100644 report/figures/train_val_loss.png diff --git a/README.md b/README.md index e10c2e83..0958d783 100644 --- a/README.md +++ b/README.md @@ -10,8 +10,8 @@ -Python package for automatic tree crown delineation based on Mask R-CNN. Pre-trained models can be picked in the [`model_garden`](https://github.com/PatBall1/detectree2/tree/master/model_garden). -A tutorial on how to prepare data, train models and make predictions is available [here](https://patball1.github.io/detectree2/tutorial.html). For questions, collaboration proposals and requests for data email [James Ball](mailto:ball.jgc@gmail.com). Some example data is available for download [here](https://doi.org/10.5281/zenodo.8136161). +Python package for automatic tree crown delineation in aerial RGB and multispectral imagery based on Mask R-CNN. Pre-trained models can be picked in the [`model_garden`](https://github.com/PatBall1/detectree2/tree/master/model_garden). +A tutorial on how to prepare data, train models and make predictions is available [here](https://patball1.github.io/detectree2/tutorial.html). For questions, collaboration proposals and requests for data email [James Ball](mailto:ball.jgc@gmail.com). Some example data is available to download [here](https://doi.org/10.5281/zenodo.8136161). Detectree2是一个基于Mask R-CNN的自动树冠检测与分割的Python包。您可以在[`model_garden`](https://github.com/PatBall1/detectree2/tree/master/model_garden)中选择预训练模型。[这里](https://patball1.github.io/detectree2/tutorial.html)提供了如何准备数据、训练模型和进行预测的教程。如果有任何问题，合作提案或者需要样例数据，可以邮件联系[James Ball](mailto:ball.jgc@gmail.com)。一些示例数据可以在[这里](https://doi.org/10.5281/zenodo.8136161)下载。 diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index 5095033f..0cf09d86 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -2,9 +2,10 @@ Tutorial ======== This tutorial goes through the steps of single class (tree) detection and -delineation. A guide to multiclass prediction (e.g. species mapping, -disease mapping) is coming soon. Example data that can be used in -this tutorial is available `here `_. +delineation from RGB and multispectral data. A guide to multiclass prediction +(e.g. species mapping, disease mapping) is coming soon. Example data that can +be used in this tutorial is available +`here `_. The key steps are: @@ -40,9 +41,15 @@ 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). +The data preparation and training process for both RGB and multispectral data +is presented here. The process is similar for both data types but there are +some key differences that are highlighted. Training a single model on both RGB +and multispectral data at the same time is not currently supported. Stick to +one data type per model (or stack the RGB bands with the multispectral bands +and treat as in the case of multispectral data). -Preparing data --------------- +Preparing data (RGB and multispectral) +-------------------------------------- An example of the recommended file structure when training a new model is as follows: @@ -58,6 +65,8 @@ An example of the recommended file structure when training a new model is as fol ├── rgb │ ├── Paracou_RGB_2016_10cm.tif (RGB orthomosaic in local UTM CRS) │ └── Paracou_RGB_2019.tif (RGB orthomosaic in local UTM CRS) + ├── ms + │ └── Paracou_MS_2016.tif (Multispectral orthomosaic in local UTM CRS) └── crowns └── UpdatedCrowns8.gpkg (Crown polygons readable by geopandas e.g. Geopackage, shapefile) @@ -65,6 +74,11 @@ Here we have two sites available to train on (Danum and Paracou). Several site d included in the training and testing phase (but only a single site directory is required). If available, several RGB orthomosaics can be included in a single site directory (see e.g ``Paracou -> RGB``). +For Paracou, we also have a multispectral scan available (5-bands). For this data, the ``mode`` parameter in the +``tile_data`` function should be set to ``"ms"``. This calls a different routine for tiling the data that retains the +``.tif`` format instead of converting to ``.png`` as in the case of ``rgb``. This comes at a slight expense of speed +later on but is necessary to retain all the multispectral information. + We call functions to from ``detectree2``'s tiling and training modules. .. code-block:: python @@ -83,9 +97,6 @@ Set up the paths to the orthomosaic and corresponding manual crown data. img_path = site_path + "/rgb/2016/Paracou_RGB_2016_10cm.tif" crown_path = site_path + "/crowns/220619_AllSpLabelled.gpkg" - # Read in the tiff file - data = rasterio.open(img_path) - # Read in crowns (then filter by an attribute if required) crowns = gpd.read_file(crown_path) crowns = crowns.to_crs(data.crs.data) # making sure CRS match @@ -98,6 +109,8 @@ The tile size will depend on: * Available computational resources. * The detail required on the crown outline. * If using a pre-trained model, the tile size used in training should roughly match the tile size of predictions. +* The ``mode`` depends on whether you are tiling 3-band RGB (``mode="rgb"``) data of multispectral data of 4 or more +bands (``mode="ms"``). .. code-block:: python @@ -114,11 +127,13 @@ surrounding tiles). Including a buffer is recommended as it allows for tiles tha Next we tile the data. The ``tile_data`` function, when ``crowns`` is supplied, will only retain tiles that contain more than the given ``threshold`` coverage of training data (here 60%). This helps to reduce the chance that the network is -trained with tiles that contain a large number of unlabelled crowns (which would reduce its sensitivity). +trained with tiles that contain a large number of unlabelled crowns (which would reduce its sensitivity). This value +should be adjusted depending on the density of crowns in the landscape (e.g. 10% may be more appropriate for savannah +type systems or urban environments). .. code-block:: python - tile_data(img_path, out_dir, buffer, tile_width, tile_height, crowns, threshold) + tile_data(img_path, out_dir, buffer, tile_width, tile_height, crowns, threshold, mode="rgb") .. warning:: If tiles are outputing as blank images set ``dtype_bool = True`` in the ``tile_data`` function. This is a bug @@ -131,7 +146,7 @@ trained with tiles that contain a large number of unlabelled crowns (which would closed canopy forests so some of the default assumptions will reflect that and parameters will need to be adjusted for different systems. -Send geojsons to train folder (with sub-folders for k-fold cross validation) and test folder. +Send geojsons to train folder (with sub-folders for k-fold cross validation) and a test folder. .. code-block:: python @@ -164,7 +179,8 @@ The data has now been tiled and partitioned for model training, tuning and evalu It is recommended to visually inspect the tiles before training to ensure that the tiling has worked as expected and -that crowns and images align. This can be done quickly with the inbuilt ``detectron2`` visualisation tools. +that crowns and images align. This can be done with the inbuilt ``detectron2`` visualisation tools. For RGB tiles +(``.png``), the following code can be used to visualise the training data. .. code-block:: python @@ -201,8 +217,61 @@ that crowns and images align. This can be done quickly with the inbuilt ``detect | -Training a model ----------------- +Alternatively, with some adaptation the ``detectron2`` visualisation tools can also be used to visualise the +multispectral (``.tif``) tiles. + +.. code-block:: python + + import rasterio + from detectron2.utils.visualizer import Visualizer + from detectree2.models.train import combine_dicts + from detectron2.data import DatasetCatalog, MetadataCatalog + from PIL import Image + import numpy as np + import cv2 + import matplotlib.pyplot as plt + from IPython.display import display + + val_fold = 1 + name = "Paracou" + tiles = "/tilesMS_" + appends + "/train" + train_location = "/content/drive/MyDrive/WORK/detectree2/data/" + name + tiles + dataset_dicts = combine_dicts(train_location, val_fold) + trees_metadata = MetadataCatalog.get(name + "_train") + + # Function to normalize and convert multi-band image to RGB if needed + def prepare_image_for_visualization(image): + if image.shape[2] == 3: + # If the image has 3 bands, assume it's RGB + image = np.stack([ + cv2.normalize(image[:, :, i], None, 0, 255, cv2.NORM_MINMAX) + for i in range(3) + ], axis=-1).astype(np.uint8) + else: + # If the image has more than 3 bands, choose the first 3 for visualization + image = image[:, :, :3] # Or select specific bands + image = np.stack([ + cv2.normalize(image[:, :, i], None, 0, 255, cv2.NORM_MINMAX) + for i in range(3) + ], axis=-1).astype(np.uint8) + + return image + + # Visualize each image in the dataset + for d in dataset_dicts: + with rasterio.open(d["file_name"]) as src: + img = src.read() # Read all bands + img = np.transpose(img, (1, 2, 0)) # Convert to HWC format + img = prepare_image_for_visualization(img) # Normalize and prepare for visualization + + visualizer = Visualizer(img[:, :, ::-1]*10, metadata=trees_metadata, scale=0.5) + out = visualizer.draw_dataset_dict(d) + image = out.get_image()[:, :, ::-1] + display(Image.fromarray(image)) + + +Training a model (RGB) +---------------------- Before training can commence, it is necessary to register the training data. It is possible to set a validation fold for model evaluation (which can be helpful for tuning models). The validation fold can be changed over different training @@ -233,7 +302,7 @@ datasets should be tuples containing strings. If just a single site is being use trains = ("Paracou_train", "Danum_train", "SepilokEast_train", "SepilokWest_train") # Registered train data tests = ("Paracou_val", "Danum_val", "SepilokEast_val", "SepilokWest_val") # Registered validation data - out_dir = "/content/drive/Shareddrives/detectree2/220809_train_outputs" + out_dir = "/content/drive/Shareddrives/detectree2/240809_train_outputs" cfg = setup_cfg(base_model, trains, tests, workers = 4, eval_period=100, max_iter=3000, out_dir=out_dir) # update_model arg can be used to load in trained model @@ -259,7 +328,7 @@ Then set up the configurations as before but with the trained model also supplie trains = ("Paracou_train", "Danum_train", "SepilokEast_train", "SepilokWest_train") # Registered train data tests = ("Paracou_val", "Danum_val", "SepilokEast_val", "SepilokWest_val") # Registered validation data - out_dir = "/content/drive/Shareddrives/detectree2/220809_train_outputs" + out_dir = "/content/drive/Shareddrives/detectree2/240809_train_outputs" cfg = setup_cfg(base_model, trains, tests, trained_model, workers = 4, eval_period=100, max_iter=3000, out_dir=out_dir) # update_model arg used to load in trained model @@ -269,7 +338,11 @@ Then set up the configurations as before but with the trained model also supplie model training will converge given the particularities of the data supplied and computational resources available. Once we are all set up, we can get commence model training. Training will continue until a specified number of -iterations (``max_iter``) or until model performance is no longer improving ("early stopping" via ``patience``). +iterations (``max_iter``) or until model performance is no longer improving ("early stopping" via ``patience``). The +``patience`` parameter sets the number of training epochs to wait for an improvement in validation accuracy before +stopping training. This is useful for preventing overfitting and saving time. Each time an improved model is found it is +saved to the output directory. + Training outputs, including model weights and training metrics, will be stored in ``out_dir``. .. code-block:: @@ -283,6 +356,154 @@ Training outputs, including model weights and training metrics, will be stored i Early stopping is implemented and will be triggered by a sustained failure to improve on the performance of predictions on the validation fold. This is measured as the AP50 score of the validation predictions. +Training a model (multispectral) +-------------------------------- + +The process for training a multispectral model is similar to that for RGB data but there are some key steps that are +different. Data will be read from ``.tif`` files of 4 or more bands instead of the 3-band ``.png`` files. + +Data should be registered as before: + +.. code-block:: python + + from detectree2.models.train import register_train_data, remove_registered_data + val_fold = 5 + appends = "40_30_0.6" + site_path = "/content/drive/SharedDrive/detectree2/data/Paracou" + train_location = site_path + "/tilesMS_" + appends + "/train/" + register_train_data(train_location, "ParacouMS", val_fold) + +The number of bands can be checked with rasterio: + +.. code-block:: python + + import rasterio + import os + import glob + + # Read in geotif and assess mean and sd for each band + #site_path = "/content/drive/MyDrive/WORK/detectree2/data/Paracou" + folder_path = site_path + "/tilesMS_" + appends + "/" + + # Select path of first .tif file + img_paths = glob.glob(folder_path + "*.tif") + img_path = img_paths[0] + + # Open the raster file + with rasterio.open(img_path) as dataset: + # Get the number of bands + num_bands = dataset.count + + # Print the number of bands + print(f'The raster has {num_bands} bands.') + + +Due to the additional bands, we must modify the weights of the first convolutional layer (conv1) to accommodate a +different number of input channels. This is done with the ``modify_conv1_weights`` function. The extension of the +``cfg.MODEL.PIXEL_MEAN`` and ``cfg.MODEL.PIXEL_STD`` lists to include the additional bands happens within the +``setup_cfg`` function when ``num_bands`` is set to a value greater than 3. ``imgmode`` should be set to ``"ms"`` to +ensure the correct training routines are called. + +.. code-block:: python + + from datetime import date + from detectron2.modeling import build_model + import torch.nn as nn + import torch.nn.init as init + from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers + import numpy as np + from detectree2.models.train import modify_conv1_weights, MyTrainer, setup_cfg + + # Good idea to keep track of the date if producing multiple models + today = date.today() + today = today.strftime("%y%m%d") + + names = ["ParacouMS",] + + trains = (names[0] + "_train",) + tests = (names[0] + "_val",) + out_dir = "/content/drive/SharedDrive/detectree2/models/" + today + "_ParacouMS" + + base_model = "COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml" # Path to the model config + + # Set up the configuration + cfg = setup_cfg(base_model, trains, tests, workers = 2, eval_period=50, + base_lr = 0.0003, backbone_freeze=0, gamma = 0.9, + max_iter=500000, out_dir=out_dir, resize = "rand_fixed", imgmode="ms", + num_bands= num_bands) # update_model arg can be used to load in trained model + + # Build the model + model = build_model(cfg) + + # Adjust input layer to accept correct number of channels + modify_conv1_weights(model, num_input_channels=num_bands) + + +With additional bands, more data is being passed through the network per image so it may be neessary to reduce the +number of images per batch. Only do this is you a getting warnings/errors about memory usage (e.g. +``CUDA out of memory``) as it will slow down training. + +.. code-block:: python + + cfg.SOLVER.IMS_PER_BATCH = 1 + + +Training can now commence as before: + +.. code-block:: + + trainer = MyTrainer(cfg, patience = 5) + trainer.resume_or_load(resume=False) + trainer.train() + + +Post-training (check training convergence) +------------------------------------------ + +It is important to check that the model has converged and is not overfitting. This can be done by plotting the training +and validation loss over time. The ``detectron2`` training routine will output a ``metrics.json`` file that can be used +to plot the training and validation loss. The following code can be used to plot the loss: + +.. code-block:: python + + import json + import matplotlib.pyplot as plt + from detectree2.models.train import load_json_arr + + #out_dir = "/content/drive/Shareddrives/detectree2/models/230103_resize_full" + experiment_folder = out_dir + + experiment_metrics = load_json_arr(experiment_folder + '/metrics.json') + + plt.plot( + [x['iteration'] for x in experiment_metrics if 'validation_loss' in x], + [x['validation_loss'] for x in experiment_metrics if 'validation_loss' in x], label='Total Validation Loss', color='red') + plt.plot( + [x['iteration'] for x in experiment_metrics if 'total_loss' in x], + [x['total_loss'] for x in experiment_metrics if 'total_loss' in x], label='Total Training Loss') + + plt.legend(loc='upper right') + plt.title('Comparison of the training and validation loss of detectree2') + plt.ylabel('Total Loss') + plt.xlabel('Number of Iterations') + plt.show() + +.. image:: ../../report/figures/train_val_loss.png + :width: 400 + :alt: Training tile 1 + :align: center + +| +Training loss and validation loss decreased over time. As training continued, the validation loss flattened whereas the +training loss continued to decrease. The ``patience`` mechanism prevented training from continuing after 3000 iterations +preventing overfitting. If validation loss is substantially higher than training loss, the model may be overfitted. + +To understand how the segmentation performance improves through training, it is also possible to plot the AP50 score +over the iterations. This can be done with the following code: + + + + Evaluating model performance ---------------------------- @@ -314,8 +535,7 @@ can discard partial the crowns predicted at the edge of tiles. site_path = "/content/drive/Shareddrives/detectree2/data/BCI_50ha" img_path = site_path + "/rgb/2015.06.10_07cm_ORTHO.tif" tiles_path = site_path + "/tilespred/" - # Read in the geotiff - data = rasterio.open(img_path) + # Location of trained model model_path = "/content/drive/Shareddrives/detectree2/models/220629_ParacouSepilokDanum_JB.pth" @@ -323,7 +543,7 @@ can discard partial the crowns predicted at the edge of tiles. buffer = 30 tile_width = 40 tile_height = 40 - tile_data(data, tiles_path, buffer, tile_width, tile_height, dtype_bool = True) + tile_data(img_path, tiles_path, buffer, tile_width, tile_height, dtype_bool = True) .. warning:: If tiles are outputing as blank images set ``dtype_bool = True`` in the ``tile_data`` function. 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32/63] tutorial updates --- docs/source/tutorial.rst | 21 ++++++++++++++++++++- report/figures/val_AP50.png | Bin 0 -> 22751 bytes 2 files changed, 20 insertions(+), 1 deletion(-) create mode 100644 report/figures/val_AP50.png diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index 0cf09d86..0370663f 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -490,7 +490,7 @@ to plot the training and validation loss. The following code can be used to plot .. image:: ../../report/figures/train_val_loss.png :width: 400 - :alt: Training tile 1 + :alt: Traina and validation loss :align: center | @@ -502,7 +502,26 @@ To understand how the segmentation performance improves through training, it is over the iterations. This can be done with the following code: +.. code-block:: python + + plt.plot( + [x['iteration'] for x in experiment_metrics if 'validation_loss' in x], + [x['validation_loss'] for x in experiment_metrics if 'validation_loss' in x], label='Total Validation Loss', color='red') + plt.plot( + [x['iteration'] for x in experiment_metrics if 'total_loss' in x], + [x['total_loss'] for x in experiment_metrics if 'total_loss' in x], label='Total Training Loss') + + plt.legend(loc='upper right') + plt.title('Comparison of the training and validation loss of detectree2') + plt.ylabel('Total Loss') + plt.xlabel('Number of Iterations') + plt.show() +.. image:: ../../report/figures/AP50.png + :width: 400 + :alt: AP50 score + :align: center +| Evaluating model performance diff --git a/report/figures/val_AP50.png b/report/figures/val_AP50.png new file mode 100644 index 0000000000000000000000000000000000000000..ec5d8ec994124e1746589ed613004c02a2ab5425 GIT binary patch literal 22751 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zYU;t7A3P3=yaM@;!9JLV>9Mj??UGh149y+`JEXl*u(mWX!w zbXXww*#>2Kxs!=Wf^xSR8KzC;FQ~^$yZEm>d(cbkX_*{2rS_+Uzoq1~#t1aXRfmTD z8kwTi913IR_bFVxCZUAc4qkqq%JF?z$IbQYjmpX|yI9SeHxHaW*!zU)EhY(nBq?_F z_A*y^{zjPTb7SN|UWS4t>-D-#Pe}9P^Qs8NDCaNF{|K=O6|ALkzJ) z17eqSS@yldO+9e|lIe}`d#Pn@EKM5qg+8MWNQmo^)1@!G)~^za5%qlx(lnmge&HN5 z*1EH_Vdg}neDHa3iHX&(I&bP%GD$c*Oh=G<#VhA5+{)UvKj#YC?(6B;P>@oxRN{83 zKX6b=BEY^dpfFdclWNB-M@_!>6m!`kyCI5umXJzr@)@*(TAI>p81Oqpn-Uo z5ry53doX>-tuXFW Date: Fri, 6 Sep 2024 19:44:11 +0100 Subject: [PATCH 33/63] multiclass --- detectree2/models/train.py | 185 ++++++++++++++--------------- detectree2/preprocessing/tiling.py | 56 ++++++--- docs/source/tutorial.rst | 44 ++++++- report/figures/IoU_AP.png | Bin 0 -> 251986 bytes report/figures/train_val_loss.png | Bin 44808 -> 41469 bytes 5 files changed, 168 insertions(+), 117 deletions(-) create mode 100644 report/figures/IoU_AP.png diff --git a/detectree2/models/train.py b/detectree2/models/train.py index d4e1a82e..b43b3555 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -532,7 +532,7 @@ def build_test_loader(cls, cfg, dataset_name): """ return build_detection_test_loader(cfg, dataset_name, mapper=FlexibleDatasetMapper(cfg, is_train=False)) -def get_tree_dicts(directory: str, classes: List[str] = None, classes_at: str = None) -> List[Dict]: +def get_tree_dicts(directory: str, class_mapping: Dict[str, int] = None) -> List[Dict]: """Get the tree dictionaries. Args: @@ -544,22 +544,14 @@ def get_tree_dicts(directory: str, classes: List[str] = None, classes_at: str = List of dictionaries corresponding to segmentations of trees. Each dictionary includes bounding box around tree and points tracing a polygon around a tree. """ - if classes is not None: - # list_of_classes = crowns[variable].unique().tolist() - classes = classes - else: - classes = ["tree"] - dataset_dicts = [] for filename in [file for file in os.listdir(directory) if file.endswith(".geojson")]: json_file = os.path.join(directory, filename) with open(json_file) as f: img_anns = json.load(f) - # Turn off type checking for annotations until we have a better solution - record: Dict[str, Any] = {} - # filename = os.path.join(directory, img_anns["imagePath"]) + record = {} filename = img_anns["imagePath"] # Make sure we have the correct height and width @@ -580,43 +572,37 @@ def get_tree_dicts(directory: str, classes: List[str] = None, classes_at: str = objs = [] for features in img_anns["features"]: anno = features["geometry"] - # pdb.set_trace() - # GenusSpecies = features['properties']['Genus_Species'] px = [a[0] for a in anno["coordinates"][0]] py = [np.array(height) - a[1] for a in anno["coordinates"][0]] - # print("### HERE IS PY ###", py) poly = [(x, y) for x, y in zip(px, py)] poly = [p for x in poly for p in x] - # print("#### HERE ARE SOME POLYS #####", poly) - if classes != ["tree"]: - obj = { - "bbox": [np.min(px), np.min(py), np.max(px), np.max(py)], - "bbox_mode": BoxMode.XYXY_ABS, - "segmentation": [poly], - "category_id": classes.index(features["properties"][classes_at]), - "iscrowd": 0, - } + + # If class mapping is provided, use it; otherwise, default to "tree" + if class_mapping: + category_id = class_mapping[features["properties"]["status"]] else: - obj = { - "bbox": [np.min(px), np.min(py), np.max(px), np.max(py)], - "bbox_mode": BoxMode.XYXY_ABS, - "segmentation": [poly], - "category_id": 0, # id - "iscrowd": 0, - } - # pdb.set_trace() + category_id = 0 # Default to "tree" if no class mapping is provided + + obj = { + "bbox": [np.min(px), np.min(py), np.max(px), np.max(py)], + "bbox_mode": BoxMode.XYXY_ABS, + "segmentation": [poly], + "category_id": category_id, + "iscrowd": 0, + } + objs.append(obj) - # print("#### HERE IS OBJS #####", objs) + record["annotations"] = objs dataset_dicts.append(record) + return dataset_dicts def combine_dicts(root_dir: str, val_dir: int, mode: str = "train", - classes: List[str] = None, - classes_at: str = None) -> List[Dict]: + class_mapping: Dict[str, int] = None) -> List[Dict]: """ Combine dictionaries from different directories based on the specified mode. @@ -631,11 +617,10 @@ def combine_dicts(root_dir: str, "train" excludes the validation directory, "val" includes only the validation directory, and "full" includes all directories. Defaults to "train". - classes (List[str], optional): A list of classes to filter the dictionaries by. Defaults to None. - classes_at (str, optional): A key to specify where the classes are located within the dictionary structure. Defaults to None. + class_mapping: A dictionary mapping class labels to category indices (optional). Returns: - List[Dict]: A list of combined dictionaries from the specified directories. + List of combined dictionaries from the specified directories. """ # Get a list of all directories within the root directory train_dirs = [os.path.join(root_dir, dir) for dir in os.listdir(root_dir)] @@ -647,15 +632,15 @@ def combine_dicts(root_dir: str, tree_dicts = [] for d in train_dirs: # Combine dictionaries from all directories except the validation directory - tree_dicts += get_tree_dicts(d, classes=classes, classes_at=classes_at) + tree_dicts += get_tree_dicts(d, class_mapping=class_mapping) elif mode == "val": # Use only the validation directory - tree_dicts = get_tree_dicts(train_dirs[(val_dir - 1)], classes=classes, classes_at=classes_at) + tree_dicts = get_tree_dicts(train_dirs[(val_dir - 1)], class_mapping=class_mapping) elif mode == "full": # Combine dictionaries from all directories, including the validation directory tree_dicts = [] for d in train_dirs: - tree_dicts += get_tree_dicts(d, classes=classes, classes_at=classes_at) + tree_dicts += get_tree_dicts(d, class_mapping=class_mapping) return tree_dicts @@ -681,35 +666,36 @@ def get_filenames(directory: str): def register_train_data(train_location, name: str = "tree", val_fold=None, - classes=None, - classes_at=None): + class_mapping_file=None): """Register data for training and (optionally) validation. Args: - train_location: directory containing training folds - name: string to name data - val_fold: fold assigned for validation and tuning. If not given, - will take place on all folds. - classes: list of classes to include - classes_at: column name for classes + train_location: Directory containing training folds. + name: Name to register the dataset. + val_fold: Validation fold index (optional). + class_mapping_file: Path to the class mapping file (json or pickle). """ + # Load the class mapping from file if provided + if class_mapping_file: + classes = load_class_mapping(class_mapping_file) + classes = list(classes.keys()) # Convert dictionary to list of class names + else: + class_mapping = None + thing_classes = ["tree"] + if val_fold is not None: for d in ["train", "val"]: - DatasetCatalog.register(name + "_" + d, lambda d=d: combine_dicts(train_location, - val_fold, d, - classes=classes, classes_at=classes_at)) - if classes is None: - MetadataCatalog.get(name + "_" + d).set(thing_classes=["tree"]) - else: - MetadataCatalog.get(name + "_" + d).set(thing_classes=classes) + DatasetCatalog.register( + name + "_" + d, + lambda d=d: combine_dicts(train_location, val_fold, d, class_mapping=class_mapping) + ) + MetadataCatalog.get(name + "_" + d).set(thing_classes=thing_classes) else: - DatasetCatalog.register(name + "_" + "full", lambda d=d: combine_dicts(train_location, - 0, "full", - classes=classes, classes_at=classes_at)) - if classes is None: - MetadataCatalog.get(name + "_" + "full").set(thing_classes=["tree"]) - else: - MetadataCatalog.get(name + "_" + "full").set(thing_classes=classes) + DatasetCatalog.register( + name + "_" + "full", + lambda d=d: combine_dicts(train_location, 0, "full", class_mapping=class_mapping) + ) + MetadataCatalog.get(name + "_" + "full").set(thing_classes=thing_classes) def get_classes(out_dir): @@ -734,6 +720,29 @@ def get_classes(out_dir): return (list) +def load_class_mapping(file_path: str): + """Function to load class-to-index mapping from a file. + + Args: + file_path: Path to the file (json or pickle) + + Returns: + class_to_idx: Loaded class-to-index mapping + """ + file_ext = Path(file_path).suffix + + if file_ext == '.json': + with open(file_path, 'r') as f: + class_to_idx = json.load(f) + elif file_ext == '.pkl': + with open(file_path, 'rb') as f: + class_to_idx = pickle.load(f) + else: + raise ValueError("Unsupported file format. Use '.json' or '.pkl'.") + + return class_to_idx + + def remove_registered_data(name="tree"): """Remove registered data from catalog. @@ -986,45 +995,25 @@ def get_latest_model_path(output_dir: str) -> str: if __name__ == "__main__": - train_location = "/content/drive/Shareddrives/detectree2/data/Paracou/tiles/train/" - register_train_data(train_location, "Paracou", 1) # folder, name, validation fold - - name = "Paracou2019" - train_location = "/content/drive/Shareddrives/detectree2/data/Paracou/tiles2019/train/" - dataset_dicts = combine_dicts(train_location, 1) - trees_metadata = MetadataCatalog.get(name + "_train") - # dataset_dicts = get_tree_dicts("./") - for d in dataset_dicts: - img = cv2.imread(d["file_name"]) - visualizer = Visualizer(img[:, :, ::-1], metadata=trees_metadata, scale=0.5) - out = visualizer.draw_dataset_dict(d) - image = cv2.cvtColor(out.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB) - # display(Image.fromarray(image)) - # Set the base (pre-trained) model from the detectron2 model_zoo - model = "COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml" - # Set the names of the registered train and test sets - # pretrained model? - # trained_model = "/content/drive/Shareddrives/detectree2/models/220629_ParacouSepilokDanum_JB.pth" - trains = ( - "Paracou_train", - "Paracou2019_train", - "ParacouUAV_train", - "Danum_train", - "SepilokEast_train", - "SepilokWest_train", - ) - tests = ( - "Paracou_val", - "Paracou2019_val", - "ParacouUAV_val", - "Danum_val", - "SepilokEast_val", - "SepilokWest_val", + # Define paths to training data and optional class mapping file + train_location = "/path/to/your/train/location" + class_mapping_file = "/path/to/your/class_to_idx.json" # Optional, can be None + + # Register the training and validation datasets using the class mapping + # If class_mapping_file is not provided, defaults to "tree" + register_train_data(train_location, "MyDataset", val_fold=1, class_mapping_file=class_mapping_file) + + # Set up model configuration, using the class mapping to determine the number of classes + cfg = setup_cfg( + base_model="COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml", + trains=("MyDataset_train", ), + tests=("MyDataset_val", ), + max_iter=3000, + out_dir="/path/to/output", + class_mapping_file=class_mapping_file # Optional ) - out_dir = "/content/drive/Shareddrives/detectree2/220703_train_outputs" - # update_model arg can be used to load in trained model - cfg = setup_cfg(model, trains, tests, eval_period=100, max_iter=3000, out_dir=out_dir) + # Train the model trainer = MyTrainer(cfg, patience=4) trainer.resume_or_load(resume=False) trainer.train() diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 3f9df300..c6b70e44 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -267,6 +267,8 @@ def process_tile_train( threshold, nan_threshold, mode: str = "rgb", + class_column: str = 'status', # Allow user to specify class column + class_mapping_file: str = None ) -> None: """Process a single tile for training data. @@ -288,6 +290,9 @@ def process_tile_train( Returns: None """ + # Load the class-to-index mapping + class_to_idx = load_class_mapping(class_mapping_file) if class_mapping_file else None + if mode == "rgb": result = process_tile(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) @@ -315,7 +320,18 @@ def process_tile_train( try: filename = out_path_root.with_suffix(".geojson") moved_scaled = overlapping_crowns.set_geometry(moved_scaled) + + # Ensure we map the selected column to the 'status' field + moved_scaled['status'] = moved_scaled[class_column] + + if class_to_idx: + moved_scaled["category_id"] = moved_scaled["status"].map(class_to_idx) + + # Save the result as GeoJSON, replacing the original class column with 'status' + moved_scaled = moved_scaled[['geometry', 'status']] # Keep only 'status' and geometry moved_scaled.to_file(driver="GeoJSON", filename=filename) + + # Add image path info to the GeoJSON file with open(filename, "r") as f: shp = json.load(f) shp.update(impath) @@ -340,6 +356,8 @@ def tile_data( nan_threshold: float = 0.1, dtype_bool: bool = False, mode: str = "rgb", + class_column: str = "status", # Allow class column to be passed here + class_mapping_file: str = None # Allow optional class mapping ) -> None: """Tiles up orthomosaic and corresponding crowns (if supplied) into training/prediction tiles. @@ -371,7 +389,7 @@ def tile_data( tile_args = [ (img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, - threshold, nan_threshold, mode) + threshold, nan_threshold, mode, class_column, class_mapping_file) for minx in np.arange(ceil(data.bounds[0]) + buffer, data.bounds[2] - tile_width - buffer, tile_width, int) for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, int) @@ -427,35 +445,41 @@ def is_overlapping_box(test_boxes_array, train_box): return False -def record_classes(crowns, - out_dir, - column='status'): - """Function that will record a list of classes into a file that can be read during training. +def record_classes(crowns: gpd.GeoDataFrame, out_dir: str, column: str = 'status', save_format: str = 'json'): + """Function that records a list of classes into a file that can be read during training. Args: crowns: gpd dataframe with the crowns out_dir: directory to save the file column: column name to get the classes from + save_format: format to save the file ('json' or 'pickle') Returns: None """ - + # Extract unique class names from the specified column list_of_classes = crowns[column].unique().tolist() - + # Sort the list of classes in alphabetical order list_of_classes.sort() - print("**The list of classes are:**") - print(list_of_classes) - print("**The list has been saved to the out_dir**") + # Create a dictionary for class-to-index mapping + class_to_idx = {class_name: idx for idx, class_name in enumerate(list_of_classes)} + + # Save the class-to-index mapping to disk + out_path = Path(out_dir) + os.makedirs(out_path, exist_ok=True) + + if save_format == 'json': + with open(out_path / 'class_to_idx.json', 'w') as f: + json.dump(class_to_idx, f) + elif save_format == 'pickle': + with open(out_path / 'class_to_idx.pkl', 'wb') as f: + pickle.dump(class_to_idx, f) + else: + raise ValueError("Unsupported save format. Use 'json' or 'pickle'.") - # Write it into file "classes.txt" - out_tif = out_dir + 'classes.txt' - f = open(out_tif, "w") - for i in list_of_classes: - f.write("%s\n" % i) - f.close() + print(f"Classes saved as {save_format} file: {class_to_idx}") def to_traintest_folders( # noqa: C901 diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index 0370663f..2f2ad214 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -490,7 +490,7 @@ to plot the training and validation loss. The following code can be used to plot .. image:: ../../report/figures/train_val_loss.png :width: 400 - :alt: Traina and validation loss + :alt: Train and validation loss :align: center | @@ -499,7 +499,7 @@ training loss continued to decrease. The ``patience`` mechanism prevented traini preventing overfitting. If validation loss is substantially higher than training loss, the model may be overfitted. To understand how the segmentation performance improves through training, it is also possible to plot the AP50 score -over the iterations. This can be done with the following code: +(see below for definition) over the iterations. This can be done with the following code: .. code-block:: python @@ -517,18 +517,56 @@ over the iterations. This can be done with the following code: plt.xlabel('Number of Iterations') plt.show() -.. image:: ../../report/figures/AP50.png +.. image:: ../../report/figures/val_AP50.png :width: 400 :alt: AP50 score :align: center | +Performance metrics +------------------- + +In instance segmentation, **AP50** refers to the **Average Precision** at an Intersection over Union (IoU) threshold of +**50%**. + +- **Precision**: Precision is the ratio of correctly predicted positive objects (true positives) to all predicted + bjects (both true positives and false positives). + + - Formula: :math:`\text{Precision} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Positives}}` + +- **Recall**: Recall is the ratio of correctly predicted positive objects (true positives) to all actual positive +objects in the ground truth (true positives and false negatives). + + - Formula: :math:`\text{Recall} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}}` + +- **Average Precision (AP)**: AP is a common metric used to evaluate the performance of object detection and instance +segmentation models. It represents the precision of the model across various recall levels. In simpler terms, it is a +combination of the model's ability to correctly detect objects and how complete those detections are. + +- **IoU (Intersection over Union)**: IoU measures the overlap between the predicted segmentation mask (or bounding box +in object detection) and the ground truth mask. It is calculated as the area of overlap divided by the area of union +between the predicted and true masks. + +- **AP50**: Specifically, **AP50** computes the average precision for all object classes at a threshold of **50% IoU**. +This means that a predicted object is considered correct (a true positive) if the IoU between the predicted and ground +truth masks is greater than or equal to 0.5 (50%). It is a relatively lenient threshold, focusing on whether the +detected objects overlap reasonably with the ground truth, even if the boundaries aren't perfectly aligned. + +In summary, AP50 evaluates how well a model detects objects with a 50% overlap between the predicted and ground truth +masks in instance segmentation tasks. + +.. image:: ../../report/figures/IoU_AP.png + :width: 400 + :alt: IoU and AP illustration + :align: center + Evaluating model performance ---------------------------- Coming soon! See Colab notebook for example routine (``detectree2/notebooks/colab/evaluationJB.ipynb``). + Generating landscape predictions -------------------------------- diff --git a/report/figures/IoU_AP.png b/report/figures/IoU_AP.png new file mode 100644 index 0000000000000000000000000000000000000000..603778b1c02958a38316db332b5748f78bc3cf8f GIT binary patch literal 251986 zcmeFYWn5d&_bwXTokF0v7Y*)STnZE|PH^|)?he7VEzlO%(&AbuUZl9YyIW5BwV(XY zxi8MWFE5|4cQSi)t!JLKX3gwqHB~t*bW(Hx0Dz?+|55`0K!klo0H7knZcRmJHX3T$Xw{9q^yIoqvI=8Hc26FPF7nX zPA*PfE*=mk7dIzAuK=$gh@X?yU5JYt#K*tGT6+#!K11vci6eGX7Qj+qZAo-*U4%xms~>3JMBxfVeog zxY%Gh*xbAw-A%pN9NlPPHT`E*FD=}_t~So@HcpOIzpFMibMkN(Wn}y(*PmT72mjNI zvxlqwAB~uUIV|igeoM^_HW1GL=%qPW$lb=>-r~PHE@|)nUn%xBzegfuZ|Z0z%IL*r zZeeNaVeif;rlMitMx|ll?BwcB#YV*`!tuLv_BQ`k`j3?VtdQe>)9fGF{%w^1mjaM< zcXzch`$q`BHTg%1$8X88|JJ9e`EN0R{zD9ZmJ*UQcl)=F{~pO}3zz>&feGUG17}lL zHw#T~XA4nAm;{_WT)`IquKm9%lyY|;02kPasN}-pGE#hO|oXNig>wr zx%hYmIe9qw1$hNP|CIUT;r}S2>Er>n{yi))u77g=EA3y|{|{>ZPjUS{)c+Lg|3`{; 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Returns: None - """ out_path = Path(out_dir) os.makedirs(out_path, exist_ok=True) From 774a15fa00338304243a11fbe0ae120d59d2ac9b Mon Sep 17 00:00:00 2001 From: James Ball Date: Fri, 6 Sep 2024 20:20:29 +0100 Subject: [PATCH 35/63] multiclass --- detectree2/models/train.py | 24 +------ detectree2/preprocessing/tiling.py | 101 ++++++++++++++++++++++------- 2 files changed, 80 insertions(+), 45 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index b43b3555..919e26a9 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -21,6 +21,7 @@ import rasterio import torch import torch.nn as nn +from detectree2.preprocessing.tiling import load_class_mapping from detectron2 import model_zoo from detectron2.checkpoint import DetectionCheckpointer # noqa:F401 from detectron2.config import get_cfg @@ -720,29 +721,6 @@ def get_classes(out_dir): return (list) -def load_class_mapping(file_path: str): - """Function to load class-to-index mapping from a file. - - Args: - file_path: Path to the file (json or pickle) - - Returns: - class_to_idx: Loaded class-to-index mapping - """ - file_ext = Path(file_path).suffix - - if file_ext == '.json': - with open(file_path, 'r') as f: - class_to_idx = json.load(f) - elif file_ext == '.pkl': - with open(file_path, 'rb') as f: - class_to_idx = pickle.load(f) - else: - raise ValueError("Unsupported file format. Use '.json' or '.pkl'.") - - return class_to_idx - - def remove_registered_data(name="tree"): """Remove registered data from catalog. diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index f1b8f17d..b4acdfe1 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -8,6 +8,7 @@ import json import logging import os +import pickle import random import shutil import warnings @@ -55,6 +56,29 @@ def get_features(gdf: gpd.GeoDataFrame): return [json.loads(gdf.to_json())["features"][0]["geometry"]] +def load_class_mapping(file_path: str): + """Function to load class-to-index mapping from a file. + + Args: + file_path: Path to the file (json or pickle) + + Returns: + class_to_idx: Loaded class-to-index mapping + """ + file_ext = Path(file_path).suffix + + if file_ext == '.json': + with open(file_path, 'r') as f: + class_to_idx = json.load(f) + elif file_ext == '.pkl': + with open(file_path, 'rb') as f: + class_to_idx = pickle.load(f) + else: + raise ValueError("Unsupported file format. Use '.json' or '.pkl'.") + + return class_to_idx + + def process_tile( img_path: str, out_dir: str, @@ -565,29 +589,62 @@ def to_traintest_folders( # noqa: C901 if __name__ == "__main__": - # Right let"s test this first with Sepilok 10cm resolution, then I need to try it with 50cm resolution. - img_path = "/content/drive/Shareddrives/detectreeRGB/benchmark/Ortho2015_benchmark/P4_Ortho_2015.tif" - crown_path = "gdrive/MyDrive/JamesHirst/NY/Buffalo/Buffalo_raw_data/all_crowns.shp" - out_dir = "./" - # Read in the tiff file - # data = img_data.open(img_path) - # Read in crowns - data = rasterio.open(img_path) + # Define paths to the input data + img_path = "/path/to/your/orthomosaic.tif" # Path to your input orthomosaic file + crown_path = "/path/to/your/crown_shapefile.shp" # Path to the shapefile containing crowns + out_dir = "/path/to/output/directory" # Directory where you want to save the tiled output + + # Optional parameters for tiling and processing + buffer = 30 # Overlap between tiles (in meters) + tile_width = 200 # Tile width (in meters) + tile_height = 200 # Tile height (in meters) + nan_threshold = 0.1 # Max proportion of tile that can be NaN before it's discarded + threshold = 0.5 # Minimum crown coverage per tile for it to be kept (0-1) + dtype_bool = False # Change dtype to uint8 to avoid black tiles + mode = "rgb" # Use 'rgb' for regular 3-channel imagery, 'ms' for multispectral + class_column = "species" # Column in the crowns file to use as the class label + + # Read in the crowns crowns = gpd.read_file(crown_path) - print( - "shape =", - data.shape, - ",", - data.bounds, - "and number of bands =", - data.count, - ", crs =", - data.crs, + + # Record the classes and save the class mapping + record_classes( + crowns=crowns, # Geopandas dataframe with crowns + out_dir=out_dir, # Output directory to save class mapping + column=class_column, # Column used for classes + save_format='json' # Choose between 'json' or 'pickle' ) - buffer = 20 - tile_width = 200 - tile_height = 200 + # Load the class-to-index mapping from the recorded classes + class_mapping_file = os.path.join(out_dir, "class_to_idx.json") + with open(class_mapping_file, 'r') as f: + class_to_idx = json.load(f) + + # Perform the tiling, ensuring the selected class column is used + tile_data( + img_path=img_path, + out_dir=out_dir, + buffer=buffer, + tile_width=tile_width, + tile_height=tile_height, + crowns=crowns, + threshold=threshold, + nan_threshold=nan_threshold, + dtype_bool=dtype_bool, + mode=mode, + class_column=class_column, # Use the selected class column (e.g., 'species', 'status') + class_mapping_file=None # We're now passing None since we don't need an external file + ) + + # Split the data into training and validation sets (optional) + # This can be used for train/test folder creation based on the generated tiles + to_traintest_folders( + tiles_folder=out_dir, # Directory where tiles are saved + out_folder="/path/to/final/data/output", # Final directory for train/test data + test_frac=0.2, # Fraction of data to be used for testing + folds=5, # Number of folds (optional, can be set to 1 for no fold splitting) + strict=True, # Ensure no overlap between train/test tiles + seed=42 # Set seed for reproducibility + ) - tile_data(data, out_dir, buffer, tile_width, tile_height, crowns) - to_traintest_folders(folds=5) + logger.info("Tiling process completed successfully!") From 993fedcbc099c3d440ec28f2e2d0ea2876307d84 Mon Sep 17 00:00:00 2001 From: James Ball Date: Sat, 7 Sep 2024 02:01:43 +0100 Subject: [PATCH 36/63] tutorial updates --- detectree2/preprocessing/tiling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index b4acdfe1..b05d0148 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -408,7 +408,7 @@ def tile_data( os.makedirs(out_path, exist_ok=True) tilename = Path(img_path).stem with rasterio.open(img_path) as data: - crs = data.crs.to_string() # Update CRS handling to avoid deprecated syntax + crs = data.crs.to_epsg() # Update CRS handling to avoid deprecated syntax tile_args = [ (img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, From 65921f99162b2d0a421b98e7485af8d0380405b7 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 9 Sep 2024 13:39:20 +0100 Subject: [PATCH 37/63] classes fix --- detectree2/models/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 919e26a9..aa728521 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -679,7 +679,7 @@ def register_train_data(train_location, # Load the class mapping from file if provided if class_mapping_file: classes = load_class_mapping(class_mapping_file) - classes = list(classes.keys()) # Convert dictionary to list of class names + thing_classes = list(classes.keys()) # Convert dictionary to list of class names else: class_mapping = None thing_classes = ["tree"] From 92b0853508e72139b82379b636eec02750945c5f Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 9 Sep 2024 14:24:21 +0100 Subject: [PATCH 38/63] classes cfg --- detectree2/models/train.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index aa728521..19b96f34 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -772,12 +772,12 @@ def setup_cfg( base_lr=0.0003389, weight_decay=0.001, max_iter=1000, - num_classes=1, eval_period=100, out_dir="./train_outputs", resize="fixed", # fixed or random or rand_fixed imgmode="rgb", num_bands=3, + class_mapping_file=None, ): """Set up config object # noqa: D417. @@ -799,8 +799,19 @@ def setup_cfg( num_classes: number of classes eval_period: number of iterations between evaluations out_dir: directory to save outputs + resize: resize strategy for images + imgmode: image mode (rgb or multispectral) + num_bands: number of bands in the image + class_mapping_file: path to class mapping file """ + # Load the class mapping if provided + if class_mapping_file: + class_mapping = load_class_mapping(class_mapping_file) + num_classes = len(class_mapping) # Set the number of classes based on the mapping + else: + num_classes = 1 # Default to 1 class if no mapping is provided + # Validate the resize parameter if resize not in {"fixed", "random", "rand_fixed"}: raise ValueError(f"Invalid resize option '{resize}'. Must be 'fixed', 'random', or 'rand_fixed'.") From d98f69f26fc3ab0b5df378c13a3ed1227240d1f6 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 9 Sep 2024 16:05:18 +0100 Subject: [PATCH 39/63] class handling --- detectree2/models/train.py | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 19b96f34..44a1108b 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -100,6 +100,9 @@ def __call__(self, dataset_dict): self.logger.warning("Received None for dataset_dict, skipping this entry.") return None + if cfg.IMGMODE == "rgb": + return super().__call__(dataset_dict) + try: # Handle multi-band image loading using rasterio with rasterio.open(dataset_dict["file_name"]) as src: @@ -116,8 +119,8 @@ def __call__(self, dataset_dict): ) # If it's a 3-band image, delegate processing to the parent class - if img.shape[-1] == 3: - return super().__call__(dataset_dict) + #if img.shape[-1] == 3: + # return super().__call__(dataset_dict) # Otherwise, handle custom multi-band logic aug_input = T.AugInput(img) @@ -624,8 +627,15 @@ def combine_dicts(root_dir: str, List of combined dictionaries from the specified directories. """ # Get a list of all directories within the root directory - train_dirs = [os.path.join(root_dir, dir) for dir in os.listdir(root_dir)] + # train_dirs = [os.path.join(root_dir, dir) for dir in os.listdir(root_dir)] + + print(class_mapping) + train_dirs = [ + os.path.join(root_dir, dir) + for dir in os.listdir(root_dir) + if os.path.isdir(os.path.join(root_dir, dir)) + ] # Handle the different modes for combining dictionaries if mode == "train": # Exclude the validation directory from the list of directories @@ -677,11 +687,12 @@ def register_train_data(train_location, class_mapping_file: Path to the class mapping file (json or pickle). """ # Load the class mapping from file if provided + class_mapping = None if class_mapping_file: - classes = load_class_mapping(class_mapping_file) - thing_classes = list(classes.keys()) # Convert dictionary to list of class names + class_mapping = load_class_mapping(class_mapping_file) + thing_classes = list(class_mapping.keys()) # Convert dictionary to list of class names + print(f"Class mapping loaded: {class_mapping}") # Debugging step else: - class_mapping = None thing_classes = ["tree"] if val_fold is not None: From 555cf0676595fc7bff8a75227a9f6b886f190c5d Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 9 Sep 2024 16:27:56 +0100 Subject: [PATCH 40/63] class handling --- detectree2/models/train.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 44a1108b..0236f4d1 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -100,7 +100,7 @@ def __call__(self, dataset_dict): self.logger.warning("Received None for dataset_dict, skipping this entry.") return None - if cfg.IMGMODE == "rgb": + if self.cfg.IMGMODE == "rgb": return super().__call__(dataset_dict) try: @@ -548,6 +548,8 @@ def get_tree_dicts(directory: str, class_mapping: Dict[str, int] = None) -> List List of dictionaries corresponding to segmentations of trees. Each dictionary includes bounding box around tree and points tracing a polygon around a tree. """ + #print("get ", class_mapping) + dataset_dicts = [] for filename in [file for file in os.listdir(directory) if file.endswith(".geojson")]: @@ -629,7 +631,7 @@ def combine_dicts(root_dir: str, # Get a list of all directories within the root directory # train_dirs = [os.path.join(root_dir, dir) for dir in os.listdir(root_dir)] - print(class_mapping) + #print(class_mapping) train_dirs = [ os.path.join(root_dir, dir) From 7fadcc756cd875d94ac8bc35fe2d1b169811d202 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 9 Sep 2024 17:27:01 +0100 Subject: [PATCH 41/63] test loader --- detectree2/models/train.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 0236f4d1..9e393f70 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -753,8 +753,17 @@ def register_test_data(test_location, name="tree"): name: string to name data """ d = "test" - DatasetCatalog.register(name + "_" + d, lambda d=d: get_tree_dicts(test_location)) - MetadataCatalog.get(name + "_" + d).set(thing_classes=["tree"]) + + class_mapping = None + if class_mapping_file: + class_mapping = load_class_mapping(class_mapping_file) + thing_classes = list(class_mapping.keys()) # Convert dictionary to list of class names + print(f"Class mapping loaded: {class_mapping}") # Debugging step + else: + thing_classes = ["tree"] + + DatasetCatalog.register(name + "_" + d, lambda d=d: get_tree_dicts(test_location, class_mapping)) + MetadataCatalog.get(name + "_" + d).set(thing_classes=thing_classes) def load_json_arr(json_path): From 516d38f7811623b72c81d0cd7397959cc4e28658 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 15:37:17 +0100 Subject: [PATCH 42/63] class columns reduced --- detectree2/preprocessing/tiling.py | 22 +++---- docs/source/index.rst | 1 + docs/source/tutorial_multi.rst | 98 ++++++++++++++++++++++++++++-- 3 files changed, 105 insertions(+), 16 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index b05d0148..55aa7897 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -291,8 +291,7 @@ def process_tile_train( threshold, nan_threshold, mode: str = "rgb", - class_column: str = 'status', # Allow user to specify class column - class_mapping_file: str = None + class_column: str = None, # Allow user to specify class column ) -> None: """Process a single tile for training data. @@ -314,9 +313,6 @@ def process_tile_train( Returns: None """ - # Load the class-to-index mapping - class_to_idx = load_class_mapping(class_mapping_file) if class_mapping_file else None - if mode == "rgb": result = process_tile(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold) @@ -345,14 +341,16 @@ def process_tile_train( filename = out_path_root.with_suffix(".geojson") moved_scaled = overlapping_crowns.set_geometry(moved_scaled) - # Ensure we map the selected column to the 'status' field - moved_scaled['status'] = moved_scaled[class_column] - - if class_to_idx: - moved_scaled["category_id"] = moved_scaled["status"].map(class_to_idx) + if class_column is not None: + # Ensure we map the selected column to the 'status' field + moved_scaled['status'] = moved_scaled[class_column] + # Keep only 'status' and geometry + moved_scaled = moved_scaled[['geometry', 'status']] + else: + # Keep only geometry + moved_scaled = moved_scaled[['geometry']] - # Save the result as GeoJSON, replacing the original class column with 'status' - moved_scaled = moved_scaled[['geometry', 'status']] # Keep only 'status' and geometry + # Save the result as GeoJSON moved_scaled.to_file(driver="GeoJSON", filename=filename) # Add image path info to the GeoJSON file diff --git a/docs/source/index.rst b/docs/source/index.rst index 255eefc3..545790ab 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -42,6 +42,7 @@ Accurate delineation of individual tree crowns in tropical forests from aerial R installation tutorial + tutorial_multi contributing using-git .. _notebooks/contributing_guide diff --git a/docs/source/tutorial_multi.rst b/docs/source/tutorial_multi.rst index 21b1fc72..9f860e42 100644 --- a/docs/source/tutorial_multi.rst +++ b/docs/source/tutorial_multi.rst @@ -4,9 +4,10 @@ Tutorial (multiclass) This tutorial goes through the steps of multiclass detection and delineation (e.g. species mapping, disease mapping). A guide to single class prediction is available -`here `_. The multiclass -process is more complicated than single class prediction as the classes need to -be correctly encoded in the data. +`here `_ - this covers +more detail on the fundamentals of training and should be reviewed before this +tutorial. The multiclassprocess is slightly more intricate than single class +prediction as the classes need to be correctly encoded and caried throughout the pipeline. The key steps are: @@ -15,4 +16,93 @@ The key steps are: 3. Evaluating model performance 4. Making landscape level predictions -THE REST OF THIS TUTORIAL IS UNDER CONSTRUCTION + + +Preparing data (RGB and multispectral) +-------------------------------------- + +Data can be prepared in a similar way to the single class case but the classes +and their order (mapping) need to be saved so that they can be accessed +consistently across training and prediction. The classes are saved in a json +file with the class names and their indices. The indices are used to encode +the classes in the training. + +.. code-block:: python + + import rasterio + import geopandas as gpd + + # Load the data + base_dir = "/content/drive/MyDrive/SHARED/detectree2" + + site_path = base_dir + "/data/Danum_lianas" + + # Set the path to the orthomosaic and the crown shapefile + img_path = site_path + "/rgb/2017_50ha_Ortho_reproject.tif" + crown_path = site_path + "/crowns/Danum_lianas_full2017.gpkg" + + # Here, we set the name of the output folder. + # Set tiling parameters + buffer = 30 + tile_width = 40 + tile_height = 40 + threshold = 0.6 + appends = str(tile_width) + "_" + str(buffer) + "_" + str(threshold) + + out_dir = site_path + "/tilesClass_" + appends + "/" + + # Read in the tiff file + data = rasterio.open(img_path) + + # Read in crowns (then filter by an attribute?) + crowns = gpd.read_file(crown_path) + crowns = crowns.to_crs(data.crs.data) + print(crowns.head()) + + class_column = 'status' + + # Record the classes and save the class mapping + record_classes( + crowns=crowns, # Geopandas dataframe with crowns + out_dir=out_dir, # Output directory to save class mapping + column=class_column, # Column to be used for classes + save_format='json' # Choose between 'json' or 'pickle' + ) + + class_mapping_file = os.path.join(out_dir, "class_to_idx.json") + +The class mapping has been saved in the output directory as a json file called +``class_to_idx.json``. This file can now be accessed to encode the classes in +training and prediction steps. + +To tile the data, we call the ``tile_data`` function as we did in the single +class case except now we point to the column name of the classes. + +.. code-block:: python + + # Tile the data + tile_data( + img_path=img_path, # Path to the orthomosaic + out_dir=out_dir, # Output directory to save tiles + buffer=buffer, # Buffer around the crowns + tile_width=tile_width, # Width of the tiles + tile_height=tile_height, # Height of the tiles + crowns=crowns, # Geopandas dataframe with crowns + threshold=threshold, # Threshold for the buffer + class_column=class_column, # Column to be used for classes + ) + + # Split the data into training and validation sets + to_traintest_folders( + tiles_folder=out_dir, # Directory where tiles are saved + out_folder=out_dir, # Final directory for train/test data + test_frac=0, # Fraction of data to be used for testing + folds=5, # Number of folds (optional, can be set to 1 for no fold splitting) + strict=False, # Ensure no overlap between train/test tiles + seed=42 # Set seed for reproducibility + ) + + +Training models +--------------- + From 09ae19a6dd3344681c6019cc6b9aa491fe275511 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 15:38:38 +0100 Subject: [PATCH 43/63] class columns reduced --- detectree2/preprocessing/tiling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 55aa7897..0eacabcf 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -347,7 +347,7 @@ def process_tile_train( # Keep only 'status' and geometry moved_scaled = moved_scaled[['geometry', 'status']] else: - # Keep only geometry + # Keep only geometry to reduce file size moved_scaled = moved_scaled[['geometry']] # Save the result as GeoJSON From db5c4b80d9b63df4c0a835d33c596158175d15a5 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 15:48:33 +0100 Subject: [PATCH 44/63] class columns reduced --- detectree2/preprocessing/tiling.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 0eacabcf..9e88f8e7 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -378,8 +378,7 @@ def tile_data( nan_threshold: float = 0.1, dtype_bool: bool = False, mode: str = "rgb", - class_column: str = "status", # Allow class column to be passed here - class_mapping_file: str = None # Allow optional class mapping + class_column: str = None, # Allow class column to be passed here ) -> None: """Tiles up orthomosaic and corresponding crowns (if supplied) into training/prediction tiles. @@ -410,7 +409,7 @@ def tile_data( tile_args = [ (img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, - threshold, nan_threshold, mode, class_column, class_mapping_file) + threshold, nan_threshold, mode, class_column) for minx in np.arange(ceil(data.bounds[0]) + buffer, data.bounds[2] - tile_width - buffer, tile_width, int) for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, int) From acc90e37e5335e724ea482a613740724c8bbff34 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 17:53:37 +0100 Subject: [PATCH 45/63] tutorial updates --- docs/source/tutorial.rst | 88 ++++++++++++++++++++++++++++++++++ docs/source/tutorial_multi.rst | 71 ++++++++++++++++++++++++++- 2 files changed, 158 insertions(+), 1 deletion(-) diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index 2f2ad214..f8863763 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -457,6 +457,94 @@ Training can now commence as before: trainer.train() +Data augmentation +----------------- + +Data augmentation is a technique used to artificially increase the size of the training dataset by applying random +transformations to the input data. This can help improve the generalization of the model and reduce overfitting. The +``detectron2`` library provides a range of data augmentation options that can be used during training. These include +random flipping, scaling, rotation, and color jittering. + +Additionally, resizing of the input data can be applied as an augmentation technique. This can be useful when training +a model that should be flexible with respect to tile size and resolution. + +By default, random rotations and flips will be performed on input images. + +.. code-block:: python + + augmentations = [ + T.RandomRotation(angle=[90, 90], expand=False), + T.RandomFlip(prob=0.4, horizontal=True, vertical=False), + T.RandomFlip(prob=0.4, horizontal=False, vertical=True), + ] + +If the input data is RGB, additional augmentations will be applied to adjust the brightness, contrast, saturation, and +lighting of the images. These augmentations are only available for RGB images and will not be applied to multispectral. + +.. code-block:: python + # Additional augmentations for RGB images + if cfg.IMGMODE == "rgb": + augmentations.extend([ + T.RandomBrightness(0.7, 1.5), + T.RandomLighting(0.7), + T.RandomContrast(0.6, 1.3), + T.RandomSaturation(0.8, 1.4) + ]) + +There are three resizing modes for the input data (1) ``fixed``, (2) ``random``, and (3) ``rand_fixed``. This are set +in the configuration file (``cfg``) with the `setup_cfg` function. + +The ``fixed`` mode will resize the input data to a images width of 1000 pixelwise + +.. code-block:: python + + if cfg.RESIZE == "fixed": + augmentations.append(T.ResizeShortestEdge([1000, 1000], 1333)) + +The ``random`` mode will randomly resize (and resample to change the resolutions) the input data to between 0.6 and 1.4 +times the original height/width. This can help the model learn to detect objects at different scales and from images of +different resolutions (and sensors). + +.. code-block:: python + + elif cfg.RESIZE == "random": + size = None + for i, datas in enumerate(DatasetCatalog.get(cfg.DATASETS.TRAIN[0])): + location = datas['file_name'] + try: + # Try to read with cv2 (for RGB images) + img = cv2.imread(location) + if img is not None: + size = img.shape[0] + else: + # Fall back to rasterio for multi-band images + with rasterio.open(location) as src: + size = src.height # Assuming square images + except Exception as e: + # Handle any errors that occur during loading + print(f"Error loading image {location}: {e}") + continue + break + + if size: + print("ADD RANDOM RESIZE WITH SIZE = ", size) + augmentations.append(T.ResizeScale(0.6, 1.4, size, size)) + +The ``rand_fixed`` mode constrains the random resizing to a fixed pixel width/height range (regardless of the resolution +of the input data). This can help to speed up training if the input tiles are high resolution and pushing up against +available memory limits. It retains the benefits of random resizing but constrains the range of possible sizes. + +.. code-block:: python + + elif cfg.RESIZE == "rand_fixed": + augmentations.append(T.ResizeScale(0.6, 1.4, 1000, 1000)) + +Which resizing option is selected depends on the problem at hand. A more precise delineation can be generated if high +resolution images are retained but this comes at the cost of increased memory usage and slower training times. If the +model is to be used on a range of different resolutions, random resizing can help the model learn to detect objects at +different scales. + + Post-training (check training convergence) ------------------------------------------ diff --git a/docs/source/tutorial_multi.rst b/docs/source/tutorial_multi.rst index 9f860e42..6c157090 100644 --- a/docs/source/tutorial_multi.rst +++ b/docs/source/tutorial_multi.rst @@ -69,7 +69,6 @@ the classes in the training. save_format='json' # Choose between 'json' or 'pickle' ) - class_mapping_file = os.path.join(out_dir, "class_to_idx.json") The class mapping has been saved in the output directory as a json file called ``class_to_idx.json``. This file can now be accessed to encode the classes in @@ -106,3 +105,73 @@ class case except now we point to the column name of the classes. Training models --------------- +To train with multiple classes, we need to ensure that the classes are +registered correctly in the dataset catalogue. This can be done with the class +mapping file that was saved in the previous step. The class mapping file will +set the classes and their indices. + +.. code-block:: python + + from detectree2.models.train import register_train_data, remove_registered_data, setup_cfg, MyTrainer + from detectree2.preprocessing.tiling import load_class_mapping + + # Set validation fold + val_fold = 5 + + site_path = base_dir + "/data/Danum_lianas" + train_dir = site_path + "/tilesClass_40_30_0.6/train" + class_mapping_file = site_path + "/tilesClass_40_30_0.6/" + "/class_to_idx.json" + data_name = "DanumLiana" + + register_train_data(train_dir, data_name, val_fold=val_fold, class_mapping_file=class_mapping_file) + + +Now the data is registered, should generate the configuration (`cfg`) and train +the model. By passing the class mapping file to the configuration set up, the +`cfg` will be register the number of classes. + +.. code-block:: python + + from detectron2.modeling import build_model + from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers + import numpy as np + from datetime import date + + + today = date.today() + today = today.strftime("%y%m%d") + + names = [data_name,] + + trains = (names[0] + "_train",) + tests = (names[0] + "_val",) + out_dir = "/content/drive/MyDrive/WORK/detectree2/models/" + today + "_Danum_lianas" + + base_model = "COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml" # Path to the model config + + # When you increase the number of channels (i.e., the number of filters) in a Convolutional Neural Network (CNN), the general recommendation is to decrease the learning rate + lrs = [0.03, 0.003, 0.0003, 0.00003] + + # Set up model configuration, using the class mapping to determine the number of classes + cfg = setup_cfg( + base_model="COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml", + trains=trains, + tests=tests, + max_iter=500000, + eval_period=50, + base_lr=lrs[0], + out_dir=out_dir, + resize="rand_fixed", + class_mapping_file=class_mapping_file # Optional + ) + + # Train the model + trainer = MyTrainer(cfg, patience=5) + trainer.resume_or_load(resume=False) + trainer.train() + + +Landscape predictions +--------------------- + +COMING SOON \ No newline at end of file From a6ccbb9d13940806021969b7b151558d1af3184d Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 17:57:23 +0100 Subject: [PATCH 46/63] tutorial updates --- docs/source/tutorial.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index f8863763..a0be35d0 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -494,7 +494,8 @@ lighting of the images. These augmentations are only available for RGB images an There are three resizing modes for the input data (1) ``fixed``, (2) ``random``, and (3) ``rand_fixed``. This are set in the configuration file (``cfg``) with the `setup_cfg` function. -The ``fixed`` mode will resize the input data to a images width of 1000 pixelwise +The ``fixed`` mode will resize the input data to a images width/height of 1000 pixels. This is efficient but may not +lead to models that transfer well across scales (e.g. if the model is to be used on a range of different resolutions). .. code-block:: python From f26e2512644fc93a9f93fa102bd77f5008d26e61 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 18:59:40 +0100 Subject: [PATCH 47/63] update test --- detectree2/tests/test_preprocessing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/detectree2/tests/test_preprocessing.py b/detectree2/tests/test_preprocessing.py index 6f2eb9cf..49a7d383 100644 --- a/detectree2/tests/test_preprocessing.py +++ b/detectree2/tests/test_preprocessing.py @@ -62,11 +62,11 @@ def test_tiling(self): tile_height = 40 threshold = 0.2 - from detectree2.preprocessing.tiling import tile_data_train + from detectree2.preprocessing.tiling import tile_data out_dir = os.path.join(out_dir, "tiles") - tile_data_train(data, out_dir, buffer, tile_width, tile_height, crowns, threshold) + tile_data(img_path, out_dir, buffer, tile_width, tile_height, crowns, threshold) # TODO: install pytest-depends to automatically order From 0fea6b47c6ff05da5af64bed77fd6c866e5e071a Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 19:31:59 +0100 Subject: [PATCH 48/63] update test --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 176f2b86..3d6173d1 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="detectree2", - version="1.0.8", + version="1.1.0", author="James G. C. Ball", author_email="ball.jgc@gmail.com", description="Detectree packaging", @@ -20,7 +20,7 @@ "pypng", "pygeos", "shapely", - "geopandas", + "geopandas>=0.14.4", "rasterio==1.3a3", "fiona", "pycrs", From f9c06de82036f053855af73f0444484283ec1f19 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 19:35:26 +0100 Subject: [PATCH 49/63] update test --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 3d6173d1..bd80e31d 100644 --- a/setup.py +++ b/setup.py @@ -20,9 +20,9 @@ "pypng", "pygeos", "shapely", - "geopandas>=0.14.4", + "geopandas", "rasterio==1.3a3", - "fiona", + "fiona==1.9.6", "pycrs", "descartes", "detectron2@git+https://github.com/facebookresearch/detectron2.git", From 77b5f2e338b80cfe9594889e9d1bc23d18aa749e Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 21:20:02 +0100 Subject: [PATCH 50/63] mypy --- detectree2/preprocessing/tiling.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index 9e88f8e7..b7d0bd9b 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -612,11 +612,6 @@ def to_traintest_folders( # noqa: C901 save_format='json' # Choose between 'json' or 'pickle' ) - # Load the class-to-index mapping from the recorded classes - class_mapping_file = os.path.join(out_dir, "class_to_idx.json") - with open(class_mapping_file, 'r') as f: - class_to_idx = json.load(f) - # Perform the tiling, ensuring the selected class column is used tile_data( img_path=img_path, @@ -629,8 +624,7 @@ def to_traintest_folders( # noqa: C901 nan_threshold=nan_threshold, dtype_bool=dtype_bool, mode=mode, - class_column=class_column, # Use the selected class column (e.g., 'species', 'status') - class_mapping_file=None # We're now passing None since we don't need an external file + class_column=class_column # Use the selected class column (e.g., 'species', 'status') ) # Split the data into training and validation sets (optional) From 31d40006928c8b1446d227958b228ddeab2c507d Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 21:30:21 +0100 Subject: [PATCH 51/63] mypy --- detectree2/models/train.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 9e393f70..55d57f29 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -536,7 +536,7 @@ def build_test_loader(cls, cfg, dataset_name): """ return build_detection_test_loader(cfg, dataset_name, mapper=FlexibleDatasetMapper(cfg, is_train=False)) -def get_tree_dicts(directory: str, class_mapping: Dict[str, int] = None) -> List[Dict]: +def get_tree_dicts(directory: str, class_mapping = None) -> List[Dict]: """Get the tree dictionaries. Args: @@ -608,7 +608,7 @@ def get_tree_dicts(directory: str, class_mapping: Dict[str, int] = None) -> List def combine_dicts(root_dir: str, val_dir: int, mode: str = "train", - class_mapping: Dict[str, int] = None) -> List[Dict]: + class_mapping = None) -> List[Dict]: """ Combine dictionaries from different directories based on the specified mode. @@ -976,7 +976,7 @@ def modify_conv1_weights(model, num_input_channels): model.backbone.bottom_up.stem.conv1.weight.copy_(new_weights) -def get_latest_model_path(output_dir: str) -> str: +def get_latest_model_path(output_dir) -> str: """ Find the model file with the highest index in the specified output directory. From e753c1aeea9017ce8f1e4954b35a098dd88c18a3 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 21:33:52 +0100 Subject: [PATCH 52/63] mypy --- detectree2/models/train.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 55d57f29..9e393f70 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -536,7 +536,7 @@ def build_test_loader(cls, cfg, dataset_name): """ return build_detection_test_loader(cfg, dataset_name, mapper=FlexibleDatasetMapper(cfg, is_train=False)) -def get_tree_dicts(directory: str, class_mapping = None) -> List[Dict]: +def get_tree_dicts(directory: str, class_mapping: Dict[str, int] = None) -> List[Dict]: """Get the tree dictionaries. Args: @@ -608,7 +608,7 @@ def get_tree_dicts(directory: str, class_mapping = None) -> List[Dict]: def combine_dicts(root_dir: str, val_dir: int, mode: str = "train", - class_mapping = None) -> List[Dict]: + class_mapping: Dict[str, int] = None) -> List[Dict]: """ Combine dictionaries from different directories based on the specified mode. @@ -976,7 +976,7 @@ def modify_conv1_weights(model, num_input_channels): model.backbone.bottom_up.stem.conv1.weight.copy_(new_weights) -def get_latest_model_path(output_dir) -> str: +def get_latest_model_path(output_dir: str) -> str: """ Find the model file with the highest index in the specified output directory. From bcc73442389867724e5a0a7bde8ac2d844ea4490 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 21:43:45 +0100 Subject: [PATCH 53/63] mypy --- detectree2/models/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 9e393f70..61e5bda8 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -572,7 +572,7 @@ def get_tree_dicts(directory: str, class_mapping: Dict[str, int] = None) -> List record["height"] = height record["width"] = width record["image_id"] = filename[0:400] - record["annotations"] = {} + record["annotations"] = [] # print(filename[0:400]) objs = [] From 534e530634226929e87e78c128ab8d8f22ae2f87 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 21:48:46 +0100 Subject: [PATCH 54/63] mypy --- detectree2/models/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 61e5bda8..6d775d96 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -572,7 +572,7 @@ def get_tree_dicts(directory: str, class_mapping: Dict[str, int] = None) -> List record["height"] = height record["width"] = width record["image_id"] = filename[0:400] - record["annotations"] = [] + # record["annotations"] = {} # print(filename[0:400]) objs = [] From c569c7c237d60f0671c90e01dd8394eb3441f223 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 22:14:53 +0100 Subject: [PATCH 55/63] mypy --- detectree2/models/train.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 6d775d96..fc0cfd57 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -536,7 +536,7 @@ def build_test_loader(cls, cfg, dataset_name): """ return build_detection_test_loader(cfg, dataset_name, mapper=FlexibleDatasetMapper(cfg, is_train=False)) -def get_tree_dicts(directory: str, class_mapping: Dict[str, int] = None) -> List[Dict]: +def get_tree_dicts(directory: str, class_mapping: Optional[Dict[str, int]] = None) -> List[Dict[str, Any]]: """Get the tree dictionaries. Args: @@ -572,7 +572,7 @@ def get_tree_dicts(directory: str, class_mapping: Dict[str, int] = None) -> List record["height"] = height record["width"] = width record["image_id"] = filename[0:400] - # record["annotations"] = {} + record["annotations"] = {} # print(filename[0:400]) objs = [] @@ -608,7 +608,7 @@ def get_tree_dicts(directory: str, class_mapping: Dict[str, int] = None) -> List def combine_dicts(root_dir: str, val_dir: int, mode: str = "train", - class_mapping: Dict[str, int] = None) -> List[Dict]: + class_mapping: Optional[Dict[str, int]] = None) -> List[Dict[str, Any]]:`` """ Combine dictionaries from different directories based on the specified mode. From 39d5b1ce1682d9cfd5ae16d139c824ac0ac05c0c Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 22:55:26 +0100 Subject: [PATCH 56/63] mypy --- detectree2/models/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index fc0cfd57..2c692f16 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -608,7 +608,7 @@ def get_tree_dicts(directory: str, class_mapping: Optional[Dict[str, int]] = Non def combine_dicts(root_dir: str, val_dir: int, mode: str = "train", - class_mapping: Optional[Dict[str, int]] = None) -> List[Dict[str, Any]]:`` + class_mapping: Optional[Dict[str, int]] = None) -> List[Dict[str, Any]]: """ Combine dictionaries from different directories based on the specified mode. From 7fdf0726a5b4403bcd3da96c579b4a7c0d1b014d Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 22:59:20 +0100 Subject: [PATCH 57/63] mypy --- detectree2/models/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index 2c692f16..eb5dbcad 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -12,7 +12,7 @@ import re import time from pathlib import Path -from typing import Any, Dict, List +from typing import Any, Dict, List, Optional import cv2 import detectron2.data.transforms as T # noqa:N812 From 9061812912188e1e35ebe587d15c9285f00d6894 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 23:08:49 +0100 Subject: [PATCH 58/63] mypy --- detectree2/models/train.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index eb5dbcad..a8f9a3a1 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -557,7 +557,7 @@ def get_tree_dicts(directory: str, class_mapping: Optional[Dict[str, int]] = Non with open(json_file) as f: img_anns = json.load(f) - record = {} + record: Dict[str, Any] = {} filename = img_anns["imagePath"] # Make sure we have the correct height and width @@ -599,7 +599,7 @@ def get_tree_dicts(directory: str, class_mapping: Optional[Dict[str, int]] = Non objs.append(obj) - record["annotations"] = objs + record["annotations"] = objs if objs else [] dataset_dicts.append(record) return dataset_dicts From c9c67655448d9c5b44782e430a49396a5e3e5281 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 23:21:13 +0100 Subject: [PATCH 59/63] mypy --- detectree2/models/train.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index a8f9a3a1..e11fd47c 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -993,7 +993,11 @@ def get_latest_model_path(output_dir: str) -> str: files = os.listdir(output_dir) # Find all files that match the pattern and extract their indices - model_files = [(f, int(model_pattern.search(f).group(1))) for f in files if model_pattern.search(f)] + model_files = [] + for f in files: + match = model_pattern.search(f) + if match: + model_files.append((f, int(match.group(1)))) if not model_files: raise FileNotFoundError(f"No model files found in the directory {output_dir}") From fc1cfe0e85377b8aa3b592acb468517490c096d0 Mon Sep 17 00:00:00 2001 From: James Ball Date: Mon, 16 Sep 2024 23:25:47 +0100 Subject: [PATCH 60/63] isort --- detectree2/data_loading/custom.py | 9 +++++---- detectree2/models/train.py | 3 ++- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/detectree2/data_loading/custom.py b/detectree2/data_loading/custom.py index c1e37c82..81a1becb 100644 --- a/detectree2/data_loading/custom.py +++ b/detectree2/data_loading/custom.py @@ -1,10 +1,11 @@ -import rasterio +import cv2 +import detectron2.data.transforms as T import numpy as np +import rasterio import torch +from detectron2.structures import BitMasks, BoxMode, Instances from torch.utils.data import Dataset -import detectron2.data.transforms as T -from detectron2.structures import BoxMode, Instances, BitMasks -import cv2 + class CustomTIFFDataset(Dataset): def __init__(self, annotations, transforms=None): diff --git a/detectree2/models/train.py b/detectree2/models/train.py index e11fd47c..faeb472e 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -21,7 +21,6 @@ import rasterio import torch import torch.nn as nn -from detectree2.preprocessing.tiling import load_class_mapping from detectron2 import model_zoo from detectron2.checkpoint import DetectionCheckpointer # noqa:F401 from detectron2.config import get_cfg @@ -43,6 +42,8 @@ from detectron2.utils.logger import log_every_n_seconds from detectron2.utils.visualizer import ColorMode, Visualizer +from detectree2.preprocessing.tiling import load_class_mapping + class FlexibleDatasetMapper(DatasetMapper): """ From dfe68e29b287fe97e458c42814dd60fd07893be3 Mon Sep 17 00:00:00 2001 From: James Ball Date: Tue, 17 Sep 2024 11:43:40 +0100 Subject: [PATCH 61/63] flake8 --- detectree2/models/train.py | 81 ++++++++++++++---------------- detectree2/preprocessing/tiling.py | 54 +++++++++++--------- 2 files changed, 66 insertions(+), 69 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index faeb472e..db8bf1e1 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -47,12 +47,13 @@ class FlexibleDatasetMapper(DatasetMapper): """ - A flexible dataset mapper that extends the standard DatasetMapper to handle multi-band images - and custom augmentations. + A flexible dataset mapper that extends the standard DatasetMapper to handle + multi-band images and custom augmentations. - This class is designed to work with datasets that may contain images with more than three channels - (e.g., multispectral images) and allows for custom augmentations to be applied. It also handles - semantic segmentation data if provided in the dataset. + This class is designed to work with datasets that may contain images with + more than three channels (e.g., multispectral images) and allows for custom + augmentations to be applied. It also handles semantic segmentation data if + provided in the dataset. Args: cfg (CfgNode): Configuration object containing dataset and model configurations. @@ -91,7 +92,7 @@ def __call__(self, dataset_dict): Process a single dataset dictionary, applying the necessary transformations and augmentations. Args: - dataset_dict (dict): A dictionary containing data for a single dataset item, including + dataset_dict (dict): A dictionary containing data for a single dataset item, including file names and metadata. Returns: @@ -116,13 +117,10 @@ def __call__(self, dataset_dict): # Size check similar to utils.check_image_size if img.shape[:2] != (dataset_dict.get("height"), dataset_dict.get("width")): self.logger.warning( - f"Image size {img.shape[:2]} does not match expected size {(dataset_dict.get('height'), dataset_dict.get('width'))}." + f"""Image size {img.shape[:2]} does not match expected size {(dataset_dict.get('height'), + dataset_dict.get('width'))}.""" ) - # If it's a 3-band image, delegate processing to the parent class - #if img.shape[-1] == 3: - # return super().__call__(dataset_dict) - # Otherwise, handle custom multi-band logic aug_input = T.AugInput(img) transforms = self.augmentations(aug_input) # Apply the augmentations @@ -152,6 +150,7 @@ def __call__(self, dataset_dict): self.logger.error(f"Error processing {file_name}: {e}") return None + class LossEvalHook(HookBase): """ A custom hook for evaluating loss during training and managing model checkpoints based on evaluation metrics. @@ -229,7 +228,7 @@ def _do_loss_eval(self): # Calculate loss for the current batch loss_batch = self._get_loss(inputs) losses.append(loss_batch) - + mean_loss = np.mean(losses) # Calculate the average AP50 across datasets if multiple datasets are used for testing @@ -318,7 +317,7 @@ class MyTrainer(DefaultTrainer): """ Custom Trainer class that extends the DefaultTrainer. - This trainer adds flexibility for handling different image types (e.g., RGB and multi-band images) + This trainer adds flexibility for handling different image types (e.g., RGB and multi-band images) and custom training behavior, such as early stopping and specialized data augmentation strategies. Args: @@ -334,7 +333,7 @@ def train(self): """ Run the training loop. - This method overrides the DefaultTrainer's train method to include early stopping and + This method overrides the DefaultTrainer's train method to include early stopping and custom logging of Average Precision (AP) metrics. Returns: @@ -397,7 +396,7 @@ def build_hooks(self): """ Build the training hooks, including the custom LossEvalHook. - This method adds a custom hook for evaluating the model's loss during training, with support for + This method adds a custom hook for evaluating the model's loss during training, with support for early stopping based on the AP50 metric. Returns: @@ -452,7 +451,7 @@ def build_train_loader(cls, cfg): """ Build the training data loader with support for custom augmentations and image types. - This method configures the data loader to apply specific augmentations depending on the image mode + This method configures the data loader to apply specific augmentations depending on the image mode (RGB or multi-band) and resize strategy defined in the configuration. Args: @@ -499,12 +498,9 @@ def build_train_loader(cls, cfg): print(f"Error loading image {location}: {e}") continue break - + if size: print("ADD RANDOM RESIZE WITH SIZE = ", size) - #min_size = int(size * 0.6) - #max_size = int(size * 1.4) - #augmentations.append(T.RandomResize(min_size=(min_size, min_size), max_size=max_size)) augmentations.append(T.ResizeScale(0.6, 1.4, size, size)) else: raise ValueError("Failed to determine image size for random resize") @@ -519,13 +515,13 @@ def build_train_loader(cls, cfg): augmentations=augmentations, ), ) - + @classmethod def build_test_loader(cls, cfg, dataset_name): """ Build the test data loader. - This method configures the data loader for evaluation, using the FlexibleDatasetMapper + This method configures the data loader for evaluation, using the FlexibleDatasetMapper to handle custom augmentations and image types. Args: @@ -537,6 +533,7 @@ def build_test_loader(cls, cfg, dataset_name): """ return build_detection_test_loader(cfg, dataset_name, mapper=FlexibleDatasetMapper(cfg, is_train=False)) + def get_tree_dicts(directory: str, class_mapping: Optional[Dict[str, int]] = None) -> List[Dict[str, Any]]: """Get the tree dictionaries. @@ -549,8 +546,7 @@ def get_tree_dicts(directory: str, class_mapping: Optional[Dict[str, int]] = Non List of dictionaries corresponding to segmentations of trees. Each dictionary includes bounding box around tree and points tracing a polygon around a tree. """ - #print("get ", class_mapping) - + dataset_dicts = [] for filename in [file for file in os.listdir(directory) if file.endswith(".geojson")]: @@ -614,26 +610,22 @@ def combine_dicts(root_dir: str, Combine dictionaries from different directories based on the specified mode. This function aggregates tree dictionaries from multiple directories within a root directory. - Depending on the mode, it either combines dictionaries from all directories, + Depending on the mode, it either combines dictionaries from all directories, all except a specified validation directory, or only from the validation directory. Args: root_dir (str): The root directory containing subdirectories with tree dictionaries. val_dir (int): The index (1-based) of the validation directory to exclude or use depending on the mode. - mode (str, optional): The mode of operation. Can be "train", "val", or "full". - "train" excludes the validation directory, - "val" includes only the validation directory, + mode (str, optional): The mode of operation. Can be "train", "val", or "full". + "train" excludes the validation directory, + "val" includes only the validation directory, and "full" includes all directories. Defaults to "train". class_mapping: A dictionary mapping class labels to category indices (optional). Returns: List of combined dictionaries from the specified directories. """ - # Get a list of all directories within the root directory - # train_dirs = [os.path.join(root_dir, dir) for dir in os.listdir(root_dir)] - - #print(class_mapping) - + # Get the list of directories within the root directory train_dirs = [ os.path.join(root_dir, dir) for dir in os.listdir(root_dir) @@ -715,7 +707,7 @@ def register_train_data(train_location, def get_classes(out_dir): """Function that will read the classes that are recorded during tiling. - + Args: out_dir: directory where classes.txt is located @@ -748,7 +740,7 @@ def remove_registered_data(name="tree"): def register_test_data(test_location, name="tree"): """Register data for testing. - + Args: test_location: directory containing test data name: string to name data @@ -769,7 +761,7 @@ def register_test_data(test_location, name="tree"): def load_json_arr(json_path): """Load json array. - + Args: json_path: path to json file """ @@ -797,7 +789,7 @@ def setup_cfg( max_iter=1000, eval_period=100, out_dir="./train_outputs", - resize="fixed", # fixed or random or rand_fixed + resize="fixed", # "fixed" or "random" or "rand_fixed" imgmode="rgb", num_bands=3, class_mapping_file=None, @@ -866,15 +858,15 @@ def setup_cfg( cfg.TEST.EVAL_PERIOD = eval_period cfg.RESIZE = resize cfg.INPUT.MIN_SIZE_TRAIN = 1000 - cfg.IMGMODE = imgmode # rgb or multispectral + cfg.IMGMODE = imgmode # "rgb" or "ms" (multispectral) if num_bands > 3: # Adjust PIXEL_MEAN and PIXEL_STD for the number of bands default_pixel_mean = cfg.MODEL.PIXEL_MEAN default_pixel_std = cfg.MODEL.PIXEL_STD # Extend or truncate the PIXEL_MEAN and PIXEL_STD based on num_bands - cfg.MODEL.PIXEL_MEAN = (default_pixel_mean * (num_bands // len(default_pixel_mean)) + + cfg.MODEL.PIXEL_MEAN = (default_pixel_mean * (num_bands // len(default_pixel_mean)) + default_pixel_mean[:num_bands % len(default_pixel_mean)]) - cfg.MODEL.PIXEL_STD = (default_pixel_std * (num_bands // len(default_pixel_std)) + + cfg.MODEL.PIXEL_STD = (default_pixel_std * (num_bands // len(default_pixel_std)) + default_pixel_std[:num_bands % len(default_pixel_std)]) return cfg @@ -887,7 +879,7 @@ def predictions_on_data(directory=None, geos_exist=True, num_predictions=0): """Prediction produced from a test folder and outputted to predictions folder. - + Args: directory: directory containing test data predictor: predictor object @@ -942,12 +934,13 @@ def predictions_on_data(directory=None, with open(output_file, "w") as dest: json.dump(evaluations, dest) + def modify_conv1_weights(model, num_input_channels): """ Modify the weights of the first convolutional layer (conv1) to accommodate a different number of input channels. - This function adjusts the weights of the `conv1` layer in the model's backbone to support a custom number - of input channels. It creates a new weight tensor with the desired number of input channels, + This function adjusts the weights of the `conv1` layer in the model's backbone to support a custom number + of input channels. It creates a new weight tensor with the desired number of input channels, and initializes it by repeating the weights of the original channels. Args: @@ -973,7 +966,7 @@ def modify_conv1_weights(model, num_input_channels): num_input_channels, old_weights.size(0), kernel_size=7, stride=2, padding=3, bias=False ) - # Copy the modified weights into the new conv1 layer + # Copy the modified weights into the new conv1 layer model.backbone.bottom_up.stem.conv1.weight.copy_(new_weights) diff --git a/detectree2/preprocessing/tiling.py b/detectree2/preprocessing/tiling.py index b7d0bd9b..2a9c1d7d 100644 --- a/detectree2/preprocessing/tiling.py +++ b/detectree2/preprocessing/tiling.py @@ -11,7 +11,7 @@ import pickle import random import shutil -import warnings +import warnings # noqa: F401 from math import ceil from pathlib import Path @@ -20,11 +20,11 @@ import numpy as np import rasterio from fiona.crs import from_epsg # noqa: F401 -from rasterio.crs import CRS +# from rasterio.crs import CRS from rasterio.errors import RasterioIOError -from rasterio.io import DatasetReader +# from rasterio.io import DatasetReader from rasterio.mask import mask -from rasterio.windows import from_bounds +# from rasterio.windows import from_bounds from shapely.geometry import box # Configure logging @@ -107,7 +107,7 @@ def process_tile( miny: Minimum y coordinate of tile crs: Coordinate reference system tilename: Name of the tile - + Returns: None """ @@ -132,7 +132,7 @@ def process_tile( return None out_img, out_transform = mask(data, shapes=coords, crop=True) - + out_sumbands = np.sum(out_img, axis=0) zero_mask = np.where(out_sumbands == 0, 1, 0) nan_mask = np.where(out_sumbands == 765, 1, 0) @@ -162,7 +162,7 @@ def process_tile( with rasterio.open(out_tif) as clipped: arr = clipped.read() r, g, b = arr[0], arr[1], arr[2] - rgb = np.dstack((b, g, r)) # Reorder for cv2 (BGRA) + rgb = np.dstack((b, g, r)) # Reorder for cv2 (BGRA) # Rescale to 0-255 if necessary if np.max(g) > 255: @@ -174,7 +174,7 @@ def process_tile( if overlapping_crowns is not None: return data, out_path_root, overlapping_crowns, minx, miny, buffer - + return data, out_path_root, None, minx, miny, buffer except RasterioIOError as e: @@ -184,6 +184,7 @@ def process_tile( logger.error(f"Error processing tile {tilename} at ({minx}, {miny}): {e}") return None + def process_tile_ms( img_path: str, out_dir: str, @@ -212,7 +213,7 @@ def process_tile_ms( miny: Minimum y coordinate of tile crs: Coordinate reference system tilename: Name of the tile - + Returns: None """ @@ -266,7 +267,7 @@ def process_tile_ms( if overlapping_crowns is not None: return data, out_path_root, overlapping_crowns, minx, miny, buffer - + return data, out_path_root, None, minx, miny, buffer except RasterioIOError as e: @@ -276,6 +277,7 @@ def process_tile_ms( logger.error(f"Error processing tile {tilename} at ({minx}, {miny}): {e}") return None + def process_tile_train( img_path: str, out_dir: str, @@ -309,19 +311,19 @@ def process_tile_train( crowns: Crown polygons as a geopandas dataframe threshold: Min proportion of the tile covered by crowns to be accepted {0,1} nan_theshold: Max proportion of tile covered by nans - + Returns: None """ if mode == "rgb": - result = process_tile(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, - crowns, threshold, nan_threshold) + result = process_tile(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, + tilename, crowns, threshold, nan_threshold) elif mode == "ms": - result = process_tile_ms(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, - crowns, threshold, nan_threshold) - + result = process_tile_ms(img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, + tilename, crowns, threshold, nan_threshold) + if result is None: - #logger.warning(f"Skipping tile at ({minx}, {miny}) due to insufficient data.") + # logger.warning(f"Skipping tile at ({minx}, {miny}) due to insufficient data.") return data, out_path_root, overlapping_crowns, minx, miny, buffer = result @@ -350,7 +352,7 @@ def process_tile_train( # Keep only geometry to reduce file size moved_scaled = moved_scaled[['geometry']] - # Save the result as GeoJSON + # Save the result as GeoJSON moved_scaled.to_file(driver="GeoJSON", filename=filename) # Add image path info to the GeoJSON file @@ -363,10 +365,12 @@ def process_tile_train( logger.warning("Cannot write empty DataFrame to file.") return + # Define a top-level helper function def process_tile_train_helper(args): return process_tile_train(*args) + def tile_data( img_path: str, out_dir: str, @@ -382,8 +386,8 @@ def tile_data( ) -> None: """Tiles up orthomosaic and corresponding crowns (if supplied) into training/prediction tiles. - Tiles up large rasters into managable tiles for training and prediction. If crowns are not supplied the function - will tile up the entire landscape for prediction. If crowns are supplied the function will tile these with the image + Tiles up large rasters into managable tiles for training and prediction. If crowns are not supplied the function + will tile up the entire landscape for prediction. If crowns are supplied the function will tile these with the image and skip tiles without a minimum coverage of crowns. The 'threshold' can be varied to ensure a good coverage of crowns across a traing tile. Tiles that do not have sufficient coverage are skipped. @@ -408,10 +412,10 @@ def tile_data( crs = data.crs.to_epsg() # Update CRS handling to avoid deprecated syntax tile_args = [ - (img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, + (img_path, out_dir, buffer, tile_width, tile_height, dtype_bool, minx, miny, crs, tilename, crowns, threshold, nan_threshold, mode, class_column) for minx in np.arange(ceil(data.bounds[0]) + buffer, data.bounds[2] - tile_width - buffer, tile_width, int) - for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, + for miny in np.arange(ceil(data.bounds[1]) + buffer, data.bounds[3] - tile_height - buffer, tile_height, int) ] @@ -479,13 +483,13 @@ def record_classes(crowns: gpd.GeoDataFrame, out_dir: str, column: str = 'status """ # Extract unique class names from the specified column list_of_classes = crowns[column].unique().tolist() - + # Sort the list of classes in alphabetical order list_of_classes.sort() # Create a dictionary for class-to-index mapping class_to_idx = {class_name: idx for idx, class_name in enumerate(list_of_classes)} - + # Save the class-to-index mapping to disk out_path = Path(out_dir) os.makedirs(out_path, exist_ok=True) @@ -536,7 +540,7 @@ def to_traintest_folders( # noqa: C901 Path(out_dir / "train").mkdir(parents=True, exist_ok=True) Path(out_dir / "test").mkdir(parents=True, exist_ok=True) - #file_names = tiles_dir.glob("*.png") + # file_names = tiles_dir.glob("*.png") file_names = tiles_dir.glob("*.geojson") file_roots = [item.stem for item in file_names] From 7a174ca049e9b41c69923f5279b1f319a4a67112 Mon Sep 17 00:00:00 2001 From: James Ball Date: Tue, 17 Sep 2024 11:52:03 +0100 Subject: [PATCH 62/63] tutorial --- docs/source/tutorial.rst | 1 + docs/source/tutorial_multi.rst | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst index a0be35d0..369d71ec 100644 --- a/docs/source/tutorial.rst +++ b/docs/source/tutorial.rst @@ -1,6 +1,7 @@ Tutorial ======== + This tutorial goes through the steps of single class (tree) detection and delineation from RGB and multispectral data. A guide to multiclass prediction (e.g. species mapping, disease mapping) is coming soon. Example data that can diff --git a/docs/source/tutorial_multi.rst b/docs/source/tutorial_multi.rst index 6c157090..125e06ef 100644 --- a/docs/source/tutorial_multi.rst +++ b/docs/source/tutorial_multi.rst @@ -17,7 +17,6 @@ The key steps are: 4. Making landscape level predictions - Preparing data (RGB and multispectral) -------------------------------------- From 1c436c3305484f15e523d8a171011b8699e69efc Mon Sep 17 00:00:00 2001 From: James Ball Date: Tue, 17 Sep 2024 12:01:40 +0100 Subject: [PATCH 63/63] pep8 --- detectree2/models/train.py | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/detectree2/models/train.py b/detectree2/models/train.py index db8bf1e1..5e404e4d 100644 --- a/detectree2/models/train.py +++ b/detectree2/models/train.py @@ -425,7 +425,7 @@ def build_hooks(self): continue break # Define augmentation based on the determined size - augmentations = [T.ResizeShortestEdge([size, size], size+300)] + augmentations = [T.ResizeShortestEdge([size, size], size + 300)] else: # Use fixed size resizing as a default augmentations = [T.ResizeShortestEdge([1000, 1000], 1333)] @@ -627,10 +627,10 @@ def combine_dicts(root_dir: str, """ # Get the list of directories within the root directory train_dirs = [ - os.path.join(root_dir, dir) - for dir in os.listdir(root_dir) - if os.path.isdir(os.path.join(root_dir, dir)) - ] + os.path.join(root_dir, dir) + for dir in os.listdir(root_dir) + if os.path.isdir(os.path.join(root_dir, dir)) + ] # Handle the different modes for combining dictionaries if mode == "train": # Exclude the validation directory from the list of directories @@ -864,10 +864,14 @@ def setup_cfg( default_pixel_mean = cfg.MODEL.PIXEL_MEAN default_pixel_std = cfg.MODEL.PIXEL_STD # Extend or truncate the PIXEL_MEAN and PIXEL_STD based on num_bands - cfg.MODEL.PIXEL_MEAN = (default_pixel_mean * (num_bands // len(default_pixel_mean)) + - default_pixel_mean[:num_bands % len(default_pixel_mean)]) - cfg.MODEL.PIXEL_STD = (default_pixel_std * (num_bands // len(default_pixel_std)) + - default_pixel_std[:num_bands % len(default_pixel_std)]) + cfg.MODEL.PIXEL_MEAN = ( + default_pixel_mean * (num_bands // len(default_pixel_mean)) + + default_pixel_mean[:num_bands % len(default_pixel_mean)] + ) + cfg.MODEL.PIXEL_STD = ( + default_pixel_std * (num_bands // len(default_pixel_std)) + + default_pixel_std[:num_bands % len(default_pixel_std)] + ) return cfg