[test] refactor samexporter_predict and get_vectorized_raster_as_geojson to avoid writing the downloaded raster

src/io/geo_helpers.py

@@ -22,42 +22,36 @@ def load_affine_transformation_from_matrix(matrix_source_coeffs: List):
         app_logger.error(f"exception:{e}, check https://github.com/rasterio/affine project for updates")
 
 
-def get_vectorized_raster_as_geojson(rio_output, mask):
-    try:
-        from rasterio import open as rio_open
-        from rasterio.features import shapes
-        from geopandas import GeoDataFrame
+def get_affine_transform_from_gdal(matrix):
+    from rasterio import Affine
 
-        app_logger.info(f"read downloaded geotiff:{rio_output} to create the shapes_generator...")
+    return Affine.from_gdal(*matrix)
 
-        with rio_open(rio_output, "r", driver="GTiff") as rio_src:
-            raster = rio_src.read()
-            transform = rio_src.transform
-            crs = rio_src.crs
 
-            app_logger.debug(f"geotiff band:{raster.shape}, type: {type(raster)}, dtype: {raster.dtype}.")
-            app_logger.debug(f"rio_src crs:{crs}.")
-            app_logger.debug(f"rio_src transform:{transform}.")
+def get_vectorized_raster_as_geojson(mask, transform):
+    try:
+        from rasterio.features import shapes
+        from geopandas import GeoDataFrame
 
-            # mask = band != 0
-            shapes_generator = ({
-                'properties': {'raster_val': v}, 'geometry': s}
-                for i, (s, v)
-                # in enumerate(shapes(mask, mask=(band != 0), transform=rio_src.transform))
-                # use mask=None to avoid using source
-                in enumerate(shapes(mask, mask=None, transform=transform))
-            )
-            app_logger.info(f"created shapes_generator, transform it to a polygon list...")
-            shapes_list = list(shapes_generator)
-            app_logger.info(f"created {len(shapes_list)} polygons.")
-            gpd_polygonized_raster = GeoDataFrame.from_features(shapes_list, crs="EPSG:3857")
-            app_logger.info(f"created a GeoDataFrame, export to geojson...")
-            geojson = gpd_polygonized_raster.to_json(to_wgs84=True)
-            app_logger.info(f"created geojson, preparing API response...")
-            return {
-                "geojson": geojson,
-                "n_shapes_geojson": len(shapes_list)
-            }
+        # mask = band != 0
+        shapes_generator = ({
+            'properties': {'raster_val': v}, 'geometry': s}
+            for i, (s, v)
+            # in enumerate(shapes(mask, mask=(band != 0), transform=rio_src.transform))
+            # use mask=None to avoid using source
+            in enumerate(shapes(mask, mask=None, transform=transform))
+        )
+        app_logger.info(f"created shapes_generator, transform it to a polygon list...")
+        shapes_list = list(shapes_generator)
+        app_logger.info(f"created {len(shapes_list)} polygons.")
+        gpd_polygonized_raster = GeoDataFrame.from_features(shapes_list, crs="EPSG:3857")
+        app_logger.info(f"created a GeoDataFrame, export to geojson...")
+        geojson = gpd_polygonized_raster.to_json(to_wgs84=True)
+        app_logger.info(f"created geojson, preparing API response...")
+        return {
+            "geojson": geojson,
+            "n_shapes_geojson": len(shapes_list)
+        }
     except Exception as e_shape_band:
         app_logger.error(f"e_shape_band:{e_shape_band}.")
         raise e_shape_band

src/prediction_api/predictors.py

@@ -1,12 +1,9 @@
 # Press the green button in the gutter to run the script.
-import tempfile
-from pathlib import Path
-
 import numpy as np
 
 from src import app_logger, MODEL_FOLDER
-from src.io.geo_helpers import get_vectorized_raster_as_geojson
-from src.io.tms2geotiff import save_geotiff_gdal, download_extent
+from src.io.geo_helpers import get_vectorized_raster_as_geojson, get_affine_transform_from_gdal
+from src.io.tms2geotiff import download_extent
 from src.prediction_api.sam_onnx import SegmentAnythingONNX
 from src.utilities.constants import MODEL_ENCODER_NAME, MODEL_DECODER_NAME, DEFAULT_TMS
 
@@ -26,26 +23,21 @@ def samexporter_predict(bbox, prompt: list[dict], zoom: float, model_name: str =
         app_logger.debug(f"using a {model_name} instance model...")
         models_instance = models_dict[model_name]["instance"]
 
-        with tempfile.TemporaryDirectory() as input_tmp_dir:
-            app_logger.info(f'tile_source: {DEFAULT_TMS}!')
-            pt0, pt1 = bbox
-            app_logger.info(f"downloading geo-referenced raster with bbox {bbox}, zoom {zoom}.")
-            img, matrix = download_extent(DEFAULT_TMS, pt0[0], pt0[1], pt1[0], pt1[1], zoom)
-            app_logger.debug(f"img type {type(img)} with shape/size:{img.size}, matrix:{matrix}.")
+        app_logger.info(f'tile_source: {DEFAULT_TMS}!')
+        pt0, pt1 = bbox
+        app_logger.info(f"downloading geo-referenced raster with bbox {bbox}, zoom {zoom}.")
+        img, matrix = download_extent(DEFAULT_TMS, pt0[0], pt0[1], pt1[0], pt1[1], zoom)
+        app_logger.info(f"img type {type(img)} with shape/size:{img.size}, matrix:{matrix}.")
 
-            pt0, pt1 = bbox
-            rio_output = str(Path(input_tmp_dir) / f"downloaded_rio_{pt0[0]}_{pt0[1]}_{pt1[0]}_{pt1[1]}.tif")
-            app_logger.debug(f"saving downloaded image as geotiff using matrix {matrix} to {rio_output}...")
-            save_geotiff_gdal(img, rio_output, matrix)
-            app_logger.info(f"saved downloaded geotiff image to {rio_output}, preparing inference...")
+        transform = get_affine_transform_from_gdal(matrix)
+        app_logger.debug(f"transform to consume with rasterio.shapes: {type(transform)}, {transform}.")
 
-            mask, prediction_masks = get_raster_inference(img, prompt, models_instance, model_name)
-            n_predictions = len(prediction_masks)
-            app_logger.info(f"created {n_predictions} masks, preparing conversion to geojson...")
-            return {
-                "n_predictions": n_predictions,
-                **get_vectorized_raster_as_geojson(rio_output, mask)
-            }
+        mask, n_predictions = get_raster_inference(img, prompt, models_instance, model_name)
+        app_logger.info(f"created {n_predictions} masks, preparing conversion to geojson...")
+        return {
+            "n_predictions": n_predictions,
+            **get_vectorized_raster_as_geojson(mask, transform)
+        }
     except ImportError as e_import_module:
         app_logger.error(f"Error trying import module:{e_import_module}.")
 
@@ -63,11 +55,12 @@ def get_raster_inference(img, prompt, models_instance, model_name):
     embedding = models_instance.encode(np_img)
     app_logger.debug(f"embedding created, running predict_masks with prompt {prompt}...")
     inference_out = models_instance.predict_masks(embedding, prompt)
-    app_logger.info(f"Created {len(inference_out)} prediction_masks,"
+    len_predictions = len(inference_out[0, :, :, :])
+    app_logger.info(f"Created {len_predictions} prediction_masks,"
                     f"shape:{inference_out.shape}, dtype:{inference_out.dtype}.")
     mask = np.zeros((inference_out.shape[2], inference_out.shape[3]), dtype=np.uint8)
     for n, m in enumerate(inference_out[0, :, :, :]):
         app_logger.debug(f"{n}th of prediction_masks shape {inference_out.shape}"
                          f" => mask shape:{mask.shape}, {mask.dtype}.")
         mask[m > 0.0] = 255
-    return mask, inference_out
+    return mask, len_predictions

tests/events/get_raster_inference.json

@@ -1,23 +1,18 @@
 {
   "europe": {
-    "input": {
-      "bbox": [[39.730430651533666, 13.52963789793414], [38.270537264557724, 10.717137897934139]],
-      "prompt": [{"type": "point", "data": [900, 481], "label": 0}],
-      "zoom": 9,
-      "model_name": "fastsam"
-    },
-    "output": {"n_predictions": 1, "geojson": "{\"type\": \"FeatureCollection\", \"features\": [{\"id\": \"0\", \"type\": \"Feature\", \"properties\": {\"raster_val\": 255.0}, \"geometry\": {\"type\": \"Polygon\", \"coordinates\": [[[13.23575362059039, 38.91882348988028], [13.23575362059039, 38.91454947738164], [13.23850020262164, 38.91454947738164], [13.23850020262164, 38.91882348988028], [13.23575362059039, 38.91882348988028]]]}}, {\"id\": \"1\", \"type\": \"Feature\", \"properties\": {\"raster_val\": 255.0}, \"geometry\": {\"type\": \"Polygon\", \"coordinates\": [[[12.97208174559039, 38.73266611280105], [12.97482832762164, 38.73266611280105], [12.97482832762164, 38.73052354396008], [12.977574909652892, 38.73052354396008], [12.977574909652892, 38.7262382134958], [12.980321491684142, 38.7262382134958], [12.980321491684142, 38.717666781467685], [12.98306807371539, 38.717666781467685], [12.98306807371539, 38.71552376282115], [12.98581465574664, 38.71552376282115], [12.98581465574664, 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tests/events/get_raster_inference/europe/img.npy

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+oid sha256:489a780d9dfc64d98d706b6203cb10d08b876113e2b7baf16147ae20dd1e0287
 size 2101376

tests/events/get_raster_inference/europe/inference_out.npy

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+oid sha256:38f8f6ee59c74a4455e461320e2194f73fc22ad5ca18b86578c3c68cd18aab00
 size 2801792

tests/events/get_raster_inference/europe/mask.npy

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 size 700544

tests/events/get_raster_inference/north_america/img.npy

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tests/events/get_raster_inference/north_america/inference_out.npy

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tests/events/get_raster_inference/north_america/mask.npy

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tests/events/get_raster_inference/oceania/img.npy

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tests/events/get_raster_inference/south_america/img.npy

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tests/events/get_raster_inference/south_america/inference_out.npy

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tests/events/get_raster_inference/south_america/mask.npy

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tests/prediction_api/test_predictors.py

@@ -30,11 +30,11 @@ def test_get_raster_inference(
             model_mocked.predict_masks.return_value = inference_out
             model_mocked.predict_masks.side_effect = None
             print(f"k:{k}.")
-            output_mask, output_inference_out = get_raster_inference(
+            output_mask, len_inference_out = get_raster_inference(
                 img=img,
                 prompt=prompt,
                 models_instance=model_mocked,
                 model_name=model_name
             )
             assert np.array_equal(output_mask, mask)
-            assert np.array_equal(output_inference_out, inference_out)
+            assert len_inference_out == input_output["output"]["n_predictions"]