[test] refactor code, fix some tests

src/io/geo_helpers.py

@@ -28,11 +28,14 @@ def get_affine_transform_from_gdal(matrix):
     return Affine.from_gdal(*matrix)
 
 
-def get_vectorized_raster_as_geojson(mask, transform):
+def get_vectorized_raster_as_geojson(mask, matrix):
     try:
         from rasterio.features import shapes
         from geopandas import GeoDataFrame
 
+        transform = get_affine_transform_from_gdal(matrix)
+        app_logger.info(f"transform to consume with rasterio.shapes: {type(transform)}, {transform}.")
+
         # mask = band != 0
         shapes_generator = ({
             'properties': {'raster_val': v}, 'geometry': s}

src/prediction_api/predictors.py

@@ -1,4 +1,7 @@
 # Press the green button in the gutter to run the script.
+import json
+import tempfile
+
 import numpy as np
 
 from src import app_logger, MODEL_FOLDER
@@ -6,11 +9,13 @@ from src.io.geo_helpers import get_vectorized_raster_as_geojson, get_affine_tran
 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
-
+from src.utilities.serialize import serialize
+from src.utilities.utilities import LogArgumentsDecorator
 
 models_dict = {"fastsam": {"instance": None}}
 
 
+@LogArgumentsDecorator.log_arguments_decorator
 def samexporter_predict(bbox, prompt: list[dict], zoom: float, model_name: str = "fastsam") -> dict:
     try:
         if models_dict[model_name]["instance"] is None:
@@ -29,14 +34,17 @@ def samexporter_predict(bbox, prompt: list[dict], zoom: float, model_name: str =
         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}.")
 
+        with tempfile.NamedTemporaryFile(mode='w', prefix=f"matrix_", delete=False) as temp_f1:
+            json.dump({"matrix": serialize(matrix)}, temp_f1)
+
         transform = get_affine_transform_from_gdal(matrix)
-        app_logger.info(f"transform to consume with rasterio.shapes: {type(transform)}, {transform}.")
+        app_logger.debug(f"transform to consume with rasterio.shapes: {type(transform)}, {transform}.")
 
         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)
+            **get_vectorized_raster_as_geojson(mask, matrix)
         }
     except ImportError as e_import_module:
         app_logger.error(f"Error trying import module:{e_import_module}.")
@@ -50,17 +58,31 @@ def get_raster_inference(img, prompt, models_instance, model_name):
         app_logger.debug(f"onnxruntime input shape (NUMPY) {np_img.shape}.")
     except Exception as e_shape:
         app_logger.error(f"e_shape:{e_shape}.")
+    try:
+        with tempfile.NamedTemporaryFile(mode='w', prefix=f"get_raster_inference__img_", delete=False) as temp_f0:
+            np.save(str(temp_f0.file.name), np_img)
+    except Exception as e_save:
+        app_logger.error(f"e_save:{e_save}.")
+        raise e_save
     app_logger.info(f"instantiated model {model_name}, ENCODER {MODEL_ENCODER_NAME}, "
                     f"DECODER {MODEL_DECODER_NAME} from {MODEL_FOLDER}: Creating embedding...")
     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)
-    len_predictions = len(inference_out[0, :, :, :])
-    app_logger.info(f"Created {len_predictions} prediction_masks,"
+    len_inference_out = len(inference_out[0, :, :, :])
+    app_logger.info(f"Created {len_inference_out} 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, len_predictions
+    try:
+        with tempfile.NamedTemporaryFile(mode='w', prefix=f"get_raster_inference__mask_", delete=False) as temp_f1:
+            np.save(temp_f1.file.name, mask)
+        with tempfile.NamedTemporaryFile(mode='w', prefix=f"get_raster_inference__inference_out_", delete=False) as temp_f2:
+            np.save(temp_f2.file.name, inference_out)
+    except Exception as e_save1:
+        app_logger.error(f"e_save1:{e_save1}.")
+        raise e_save1
+    return mask, len_inference_out

tests/events/{get_raster_inference.json → samexporter_predict.json}

@@ -8,8 +8,8 @@
     "output": {"n_predictions": 1, "geojson": "{\"type\": \"FeatureCollection\", \"features\": [{\"id\": \"0\", \"type\": \"Feature\", \"properties\": {\"raster_val\": 255.0}, \"geometry\": {\"type\": \"Polygon\", \"coordinates\": [[[-114.19189453125001, 47.99727386804473], [-114.10400390625001, 47.99727386804473], [-114.10400390625001, 47.98992166741417], [-114.09301757812501, 47.98992166741417], [-114.09301757812501, 47.96050238891508], [-114.10400390625001, 47.96050238891508], [-114.10400390625001, 47.93842692948105], [-114.09301757812501, 47.93842692948105], [-114.09301757812501, 47.87951293397049], [-114.08203125000001, 47.87951293397049], [-114.08203125000001, 47.77625204393234], [-114.071044921875, 47.77625204393234], [-114.071044921875, 47.70976154266638], [-114.08203125000001, 47.70976154266638], [-114.08203125000001, 47.70236846657371], [-114.09301757812501, 47.70236846657371], [-114.09301757812501, 47.69497434186281], [-114.10400390625001, 47.69497434186281], [-114.10400390625001, 47.68757916850812], [-114.13696289062501, 47.68757916850812], [-114.13696289062501, 47.69497434186281], [-114.158935546875, 47.69497434186281], [-114.158935546875, 47.70236846657371], [-114.18090820312501, 47.70236846657371], [-114.18090820312501, 47.70976154266638], [-114.19189453125001, 47.70976154266638], [-114.19189453125001, 47.724544549099654], [-114.20288085937501, 47.724544549099654], [-114.20288085937501, 47.73932336136855], [-114.21386718750001, 47.73932336136855], [-114.21386718750001, 47.74671119475599], [-114.23583984375, 47.74671119475599], [-114.23583984375, 47.75409797968002], [-114.24682617187501, 47.75409797968002], [-114.24682617187501, 47.76148371616668], [-114.2578125, 47.76148371616668], [-114.2578125, 47.78363463526376], [-114.26879882812501, 47.78363463526376], [-114.26879882812501, 47.791016178262595], [-114.27978515625001, 47.791016178262595], [-114.27978515625001, 47.79839667295523], [-114.30175781250001, 47.79839667295523], [-114.30175781250001, 47.82053186746052], [-114.31274414062501, 47.82053186746052], [-114.31274414062501, 47.82790816919328], [-114.30175781250001, 47.82790816919328], [-114.30175781250001, 47.83528342275263], [-114.29077148437501, 47.83528342275263], [-114.29077148437501, 47.842657628165355], [-114.27978515625001, 47.842657628165355], [-114.27978515625001, 47.850030785458266], [-114.26879882812501, 47.850030785458266], [-114.26879882812501, 47.85740289465823], [-114.2578125, 47.85740289465823], [-114.2578125, 47.86477395579223], [-114.24682617187501, 47.86477395579223], [-114.24682617187501, 47.87214396888729], [-114.23583984375, 47.87214396888729], [-114.23583984375, 47.87951293397049], [-114.22485351562503, 47.87951293397049], [-114.22485351562503, 47.89424772020997], [-114.21386718750001, 47.89424772020997], [-114.21386718750001, 47.91634204016117], [-114.20288085937501, 47.91634204016117], [-114.20288085937501, 47.98992166741417], [-114.19189453125001, 47.98992166741417], [-114.19189453125001, 47.99727386804473]]]}}, {\"id\": \"1\", \"type\": \"Feature\", \"properties\": {\"raster_val\": 0.0}, \"geometry\": {\"type\": \"Polygon\", \"coordinates\": [[[-124.46411132812499, 51.62483746174321], [-124.46411132812499, 46.71726868507395], [-113.214111328125, 46.71726868507395], [-113.214111328125, 51.62483746174321], [-124.46411132812499, 51.62483746174321]], [[-114.19189453125001, 47.99727386804473], [-114.19189453125001, 47.98992166741417], [-114.20288085937501, 47.98992166741417], [-114.20288085937501, 47.91634204016117], [-114.21386718750001, 47.91634204016117], [-114.21386718750001, 47.89424772020997], [-114.22485351562503, 47.89424772020997], [-114.22485351562503, 47.87951293397049], [-114.23583984375, 47.87951293397049], [-114.23583984375, 47.87214396888729], [-114.24682617187501, 47.87214396888729], [-114.24682617187501, 47.86477395579223], [-114.2578125, 47.86477395579223], [-114.2578125, 47.85740289465823], [-114.26879882812501, 47.85740289465823], [-114.26879882812501, 47.850030785458266], [-114.27978515625001, 47.850030785458266], [-114.27978515625001, 47.842657628165355], [-114.29077148437501, 47.842657628165355], [-114.29077148437501, 47.83528342275263], [-114.30175781250001, 47.83528342275263], [-114.30175781250001, 47.82790816919328], [-114.31274414062501, 47.82790816919328], [-114.31274414062501, 47.82053186746052], [-114.30175781250001, 47.82053186746052], [-114.30175781250001, 47.79839667295523], [-114.27978515625001, 47.79839667295523], [-114.27978515625001, 47.791016178262595], [-114.26879882812501, 47.791016178262595], [-114.26879882812501, 47.78363463526376], [-114.2578125, 47.78363463526376], [-114.2578125, 47.76148371616668], [-114.24682617187501, 47.76148371616668], [-114.24682617187501, 47.75409797968002], [-114.23583984375, 47.75409797968002], [-114.23583984375, 47.74671119475599], [-114.21386718750001, 47.74671119475599], [-114.21386718750001, 47.73932336136855], [-114.20288085937501, 47.73932336136855], [-114.20288085937501, 47.724544549099654], [-114.19189453125001, 47.724544549099654], [-114.19189453125001, 47.70976154266638], [-114.18090820312501, 47.70976154266638], [-114.18090820312501, 47.70236846657371], [-114.158935546875, 47.70236846657371], [-114.158935546875, 47.69497434186281], [-114.13696289062501, 47.69497434186281], [-114.13696289062501, 47.68757916850812], [-114.10400390625001, 47.68757916850812], [-114.10400390625001, 47.69497434186281], [-114.09301757812501, 47.69497434186281], [-114.09301757812501, 47.70236846657371], [-114.08203125000001, 47.70236846657371], [-114.08203125000001, 47.70976154266638], [-114.071044921875, 47.70976154266638], [-114.071044921875, 47.77625204393234], [-114.08203125000001, 47.77625204393234], [-114.08203125000001, 47.87951293397049], [-114.09301757812501, 47.87951293397049], [-114.09301757812501, 47.93842692948105], [-114.10400390625001, 47.93842692948105], [-114.10400390625001, 47.96050238891508], [-114.09301757812501, 47.96050238891508], [-114.09301757812501, 47.98992166741417], [-114.10400390625001, 47.98992166741417], [-114.10400390625001, 47.99727386804473], [-114.19189453125001, 47.99727386804473]]]}}]}", "n_shapes_geojson": 2}
   },
   "oceania": {
-    "input": {"matrix": [7269467.138033403, 9783.93962050256, 0, -166326.9735485418, 0, -9783.939620502566], "bbox": [[-1.4939713066293112, 155.30273437500003], [-52.32191088594772, 65.30273437500001]], "prompt": [{"type": "point", "data": [934, 511], "label": 0}], "zoom": 4, "model_name": "fastsam"},
-    "output": {"n_predictions": 1, "geojson": "{\"type\": \"FeatureCollection\", \"features\": [{\"id\": \"0\", \"type\": \"Feature\", \"properties\": {\"raster_val\": 255.0}, \"geometry\": {\"type\": \"Polygon\", \"coordinates\": [[[148.095703125, -40.11168866559596], [148.447265625, -40.11168866559596], [148.447265625, -40.313043208880906], [148.359375, -40.313043208880906], [148.359375, -40.38002840251183], [148.447265625, -40.38002840251183], [148.447265625, -40.4469470596005], [148.53515625, -40.4469470596005], [148.53515625, -40.580584664127635], [148.623046875, -40.580584664127635], [148.623046875, -40.91351257612758], [148.7109375, -40.91351257612758], [148.7109375, -41.04621681452063], [148.623046875, -41.04621681452063], [148.623046875, -42.682435398386225], [148.53515625, -42.682435398386225], [148.53515625, -43.004647127794435], [148.447265625, -43.004647127794435], [148.447265625, -43.197167282501276], [148.359375, -43.197167282501276], [148.359375, -43.32517767999296], 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[144.84375000000003, -41.83682786072714], [144.755859375, -41.83682786072714], [144.755859375, -41.70572851523752], [144.66796875000003, -41.70572851523752], [144.66796875000003, -41.64007838467893], [144.580078125, -41.64007838467893], [144.580078125, -41.44272637767212], [144.4921875, -41.44272637767212], [144.4921875, -41.112468789180866], [144.404296875, -41.112468789180866], [144.404296875, -40.847060356071225], [144.31640625, -40.847060356071225], [144.31640625, -40.64730356252252], [144.404296875, -40.64730356252252], [144.404296875, -40.580584664127635], [144.4921875, -40.580584664127635], [144.4921875, -40.51379915504414], [144.84375000000003, -40.51379915504414], [144.84375000000003, -40.580584664127635], [145.37109375000003, -40.580584664127635], [145.37109375000003, -40.64730356252252], [145.54687500000003, -40.64730356252252], [145.54687500000003, -40.71395582628605], [145.72265625, -40.71395582628605], [145.72265625, -40.78054143186032], [145.8984375, -40.78054143186032], 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   },
   "south_america": {
     "input": {"matrix": [-7922544.351904369, 305.74811314070394, 0, -5432228.234830927, 0, -305.7481131407035], "bbox": [[-43.78498531802787, -68.35692680430485], [-45.12587626673896, -71.16942680430483]], "prompt": [{"type": "point", "data": [917, 492], "label": 0}], "zoom": 9, "model_name": "fastsam"},

tests/events/{get_raster_inference → samexporter_predict}/oceania/img.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3cb7c28cdbf2828fc427940364a9bc9ddab2e8fe873c011a8e948bcab164adff
+oid sha256:cdf88681e56a3ea0731f44f8e2f95c30f2d5128fe8b08f218334893a1a9c9dc6
 size 2101376

tests/events/samexporter_predict/oceania/inference_out.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cfd4d9c88463dad5a0d93c9aff912934cb7f9111c03671d9942904de4c58cf99
+size 2801792

tests/events/{get_raster_inference → samexporter_predict}/oceania/mask.npy

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b9648d6cf9e8dddf39a3521948d90c708847779df6160409b3bd67309f7c1005
+oid sha256:03d395e62a03fc952746e05c4682d92952d4d66f2825545afd83e7a7f077e5c0
 size 700544

tests/io/test_coordinates_pixel_conversion.py

@@ -1,13 +1,56 @@
-from tests.utilities_testing import fn_reading_json_inputs_outputs__test
+import json
+
+from aws_lambda_powertools.utilities.parser import parse
+
+from src.io.coordinates_pixel_conversion import get_latlng2pixel_projection, get_point_latlng_to_pixel_coordinates, \
+    get_latlng_to_pixel_coordinates
+from src.utilities.type_hints import LatLngDict
+from tests import TEST_EVENTS_FOLDER
 
 
 def test_get_latlng2pixel_projection():
-    fn_reading_json_inputs_outputs__test(name_fn="get_latlng2pixel_projection")
+    name_fn = "get_latlng2pixel_projection"
+
+    with open(TEST_EVENTS_FOLDER / f"{name_fn}.json") as tst_json:
+        inputs_outputs = json.load(tst_json)
+        for k, input_output in inputs_outputs.items():
+            print(f"k:{k}")
+            current_input = input_output["input"]
+            latlng_input = parse(event=current_input["latlng"], model=LatLngDict)
+            output = get_latlng2pixel_projection(latlng_input)
+            assert output == input_output["output"]
 
 
 def test_get_point_latlng_to_pixel_coordinates():
-    fn_reading_json_inputs_outputs__test(name_fn="get_point_latlng_to_pixel_coordinates")
+    name_fn = "get_point_latlng_to_pixel_coordinates"
+
+    with open(TEST_EVENTS_FOLDER / f"{name_fn}.json") as tst_json:
+        inputs_outputs = json.load(tst_json)
+        for k, input_output in inputs_outputs.items():
+            print(f"k:{k}")
+            current_input = input_output["input"]
+            latlng_input = parse(event=current_input["latlng"], model=LatLngDict)
+            output = get_point_latlng_to_pixel_coordinates(latlng=latlng_input, zoom=current_input["zoom"])
+            assert output == input_output["output"]
 
 
 def test_get_latlng_to_pixel_coordinates():
-    fn_reading_json_inputs_outputs__test(name_fn="get_latlng_to_pixel_coordinates")
+    name_fn = "get_latlng_to_pixel_coordinates"
+
+    with open(TEST_EVENTS_FOLDER / f"{name_fn}.json") as tst_json:
+        inputs_outputs = json.load(tst_json)
+        for k, input_output in inputs_outputs.items():
+            print(f"k:{k}")
+            current_input = input_output["input"]
+            zoom = current_input["zoom"]
+            latlng_origin_ne = parse(event=current_input['latlng_origin_ne'], model=LatLngDict)
+            latlng_origin_sw = parse(event=current_input['latlng_origin_sw'], model=LatLngDict)
+            latlng_current_point = parse(event=current_input['latlng_current_point'], model=LatLngDict)
+            output = get_latlng_to_pixel_coordinates(
+                latlng_origin_ne=latlng_origin_ne,
+                latlng_origin_sw=latlng_origin_sw,
+                latlng_current_point=latlng_current_point,
+                zoom=zoom,
+                k=k
+            )
+            assert output == input_output["output"]

tests/io/test_geo_helpers.py

@@ -0,0 +1,23 @@
+import json
+import numpy as np
+import shapely
+
+from tests import TEST_EVENTS_FOLDER
+
+
+def test_get_vectorized_raster_as_geojson():
+    from src.io.geo_helpers import get_vectorized_raster_as_geojson
+
+    name_fn = "samexporter_predict"
+
+    with open(TEST_EVENTS_FOLDER / f"{name_fn}.json") as tst_json:
+        inputs_outputs = json.load(tst_json)
+        for k, input_output in inputs_outputs.items():
+            print(f"k:{k}.")
+            mask = np.load(TEST_EVENTS_FOLDER / name_fn / k / "mask.npy")
+
+            output = get_vectorized_raster_as_geojson(mask=mask, matrix=input_output["input"]["matrix"])
+            assert output["n_shapes_geojson"] == input_output["output"]["n_shapes_geojson"]
+            output_geojson = shapely.from_geojson(output["geojson"])
+            expected_output_geojson = shapely.from_geojson(input_output["output"]["geojson"])
+            assert shapely.equals_exact(output_geojson, expected_output_geojson, tolerance=0.000006)

tests/prediction_api/test_predictors.py

@@ -12,7 +12,7 @@ from tests import TEST_EVENTS_FOLDER
 def test_get_raster_inference(
     segment_anything_onnx_mocked
 ):
-    name_fn = "get_raster_inference"
+    name_fn = "samexporter_predict"
 
     with open(TEST_EVENTS_FOLDER / f"{name_fn}.json") as tst_json:
         inputs_outputs = json.load(tst_json)

tests/utilities_testing.py

@@ -1,26 +0,0 @@
-import json
-
-from src.io.coordinates_pixel_conversion import get_latlng2pixel_projection, get_point_latlng_to_pixel_coordinates, \
-    get_latlng_to_pixel_coordinates
-from src.io.lambda_helpers import get_parsed_bbox_points
-
-from tests import TEST_EVENTS_FOLDER
-
-
-names_fn_dict = {
-    "get_latlng2pixel_projection": get_latlng2pixel_projection,
-    "get_point_latlng_to_pixel_coordinates": get_point_latlng_to_pixel_coordinates,
-    "get_latlng_to_pixel_coordinates": get_latlng_to_pixel_coordinates,
-    "get_parsed_bbox_points": get_parsed_bbox_points
-}
-
-
-def fn_reading_json_inputs_outputs__test(name_fn):
-    fn = names_fn_dict[name_fn]
-
-    with open(TEST_EVENTS_FOLDER / f"{name_fn}.json") as tst_json:
-        inputs_outputs = json.load(tst_json)
-        for k, input_output in inputs_outputs.items():
-            print(f"k:{k}.")
-            output = fn(**input_output["input"])
-            assert output == input_output["output"]