[feat] add option to dump temp images during ML recognition operations

README.md

@@ -133,6 +133,7 @@ It's possible to use the project in a bare metal installation (not within a dock
   export PYTORCH_KERNEL_CACHE_PATH="$HOME/.cache/torch/kernels"
   export FASTAPI_STATIC="$HOME/workspace/samgis-lisa-on-cuda/static"
   export VIS_OUTPUT="$HOME/workspace/samgis-lisa-on-cuda/vis_output"
+  export WRITE_TMP_ON_DISK=${VIS_OUTPUT}
   ```
 
 - execute the script `baremetal_entrypoint.sh` instead than `docker_entrypoint.sh`.

samgis_lisa_on_cuda/io/raster_helpers.py

@@ -1,8 +1,11 @@
 """helpers for computer vision duties"""
 import numpy as np
 from numpy import ndarray, bitwise_not
+from rasterio import open as rasterio_open
 
+from samgis_lisa_on_cuda import PROJECT_ROOT_FOLDER
 from samgis_lisa_on_cuda import app_logger
+from samgis_lisa_on_cuda.utilities.constants import OUTPUT_CRS_STRING
 from samgis_lisa_on_cuda.utilities.type_hints import XYZTerrainProvidersNames
 
 
@@ -292,3 +295,37 @@ def check_empty_array(arr: ndarray, val: float) -> bool:
     check5 = np.array_equal(arr_check5_tmp, arr_check5)
     app_logger.debug(f"array checks:{check1}, {check2}, {check3}, {check4}, {check5}.")
     return check1 or check2 or check3 or check4 or check5
+
+
+def write_raster_png(arr, transform, prefix: str, suffix: str, folder_output_path="/tmp"):
+    from pathlib import Path
+    from rasterio.plot import reshape_as_raster
+
+    output_filename = Path(folder_output_path) / f"{prefix}_{suffix}.png"
+
+    with rasterio_open(
+            output_filename, 'w', driver='PNG',
+            height=arr.shape[0],
+            width=arr.shape[1],
+            count=3,
+            dtype=str(arr.dtype),
+            crs=OUTPUT_CRS_STRING,
+            transform=transform) as dst:
+        dst.write(reshape_as_raster(arr))
+    app_logger.info(f"written:{output_filename} as PNG, use {OUTPUT_CRS_STRING} as CRS.")
+
+
+def write_raster_tiff(arr, transform, prefix: str, suffix: str, folder_output_path="/tmp"):
+    from pathlib import Path
+    output_filename = Path(folder_output_path) / f"{prefix}_{suffix}.tiff"
+
+    with rasterio_open(
+            output_filename, 'w', driver='GTiff',
+            height=arr.shape[0],
+            width=arr.shape[1],
+            count=1,
+            dtype=str(arr.dtype),
+            crs=OUTPUT_CRS_STRING,
+            transform=transform) as dst:
+        dst.write(arr, 1)
+    app_logger.info(f"written:{output_filename} as TIFF, use {OUTPUT_CRS_STRING} as CRS.")

samgis_lisa_on_cuda/prediction_api/lisa.py

@@ -1,10 +1,15 @@
+from datetime import datetime
+
+from lisa_on_cuda.utils import app_helpers
+from samgis_core.utilities.type_hints import llist_float, dict_str_int
 from samgis_lisa_on_cuda import app_logger
 from samgis_lisa_on_cuda.io.geo_helpers import get_vectorized_raster_as_geojson
+from samgis_lisa_on_cuda.io.raster_helpers import write_raster_png, write_raster_tiff
 from samgis_lisa_on_cuda.io.tms2geotiff import download_extent
 from samgis_lisa_on_cuda.prediction_api.global_models import models_dict
 from samgis_lisa_on_cuda.utilities.constants import DEFAULT_URL_TILES
-from samgis_core.utilities.type_hints import llist_float, list_dict, dict_str_int
-from lisa_on_cuda.utils import app_helpers
+
+msg_write_tmp_on_disk = "found option to write images and geojson output..."
 
 
 def lisa_predict(
@@ -32,6 +37,8 @@ def lisa_predict(
     Returns:
         Affine transform
     """
+    from os import getenv
+
     app_logger.info("start lisa inference...")
     if models_dict[inference_function_name_key]["inference"] is None:
         app_logger.info(f"missing inference function {inference_function_name_key}, instantiating it now!")
@@ -44,9 +51,19 @@ def lisa_predict(
     pt0, pt1 = bbox
     app_logger.info(f"tile_source: {source}: downloading geo-referenced raster with bbox {bbox}, zoom {zoom}.")
     img, transform = download_extent(w=pt1[1], s=pt1[0], e=pt0[1], n=pt0[0], zoom=zoom, source=source)
-
     app_logger.info(
         f"img type {type(img)} with shape/size:{img.size}, transform type: {type(transform)}, transform:{transform}.")
+    folder_write_tmp_on_disk = getenv("WRITE_TMP_ON_DISK", "")
+    if bool(folder_write_tmp_on_disk):
+        now = datetime.now().strftime('%Y%m%d_%H%M%S')
+        prefix = f"w{pt1[1]},s{pt1[0]},e{pt0[1]},n{pt0[0]}_"
+        app_logger.info(msg_write_tmp_on_disk + f"with coords {prefix}, shape:{img.shape}, {len(img.shape)}.")
+        if img.shape and len(img.shape) == 2:
+            write_raster_tiff(img, transform, f"{prefix}_{now}_", f"raw_tiff", folder_write_tmp_on_disk)
+        if img.shape and len(img.shape) == 3 and img.shape[2] == 3:
+            write_raster_png(img, transform, f"{prefix}_{now}_", f"raw_img", folder_write_tmp_on_disk)
+    else:
+        app_logger.info("keep all temp data in memory...")
 
     _, mask, output_string = inference_fn(prompt, img)
     # app_logger.info(f"created {n_predictions} masks, preparing conversion to geojson...")