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aletrn commited on Jan 27, 2024

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768585c

1 Parent(s): 039b373

[refactor] move core instance segmentation (segment anything) functionality to samgis_core, bump to version 1.2.0

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Files changed (37) hide show

.idea/vcs.xml +1 -0
docs/Changelog.md +4 -0
docs/conf.py +2 -2
docs/modules.rst +1 -0
docs/samgis.prediction_api.rst +0 -8
docs/samgis.utilities.rst +0 -24
docs/specs/openapi.yaml +3 -2
poetry.lock +82 -62
pyproject.toml +6 -5
requirements.txt +0 -18
requirements_dev.txt +0 -17
requirements_dockerfile.txt +0 -14
samgis/__init__.py +1 -1
samgis/__version__.py +1 -1
samgis/io/coordinates_pixel_conversion.py +3 -1
samgis/io/geo_helpers.py +1 -1
samgis/io/raster_helpers.py +4 -4
samgis/io/tms2geotiff.py +4 -1
samgis/io/wrappers_helpers.py +1 -1
samgis/prediction_api/predictors.py +7 -45
samgis/prediction_api/sam_onnx.py +0 -246
samgis/utilities/constants.py +0 -3
samgis/utilities/fastapi_logger.py +0 -24
samgis/utilities/serialize.py +0 -84
samgis/utilities/type_hints.py +0 -13
samgis/utilities/utilities.py +0 -92
tests/io/test_coordinates_pixel_conversion.py +1 -28
tests/io/test_geo_helpers.py +0 -1
tests/io/test_lambda_helpers.py +0 -133
tests/io/test_raster_helpers.py +2 -2
tests/io/test_tms2geotiff.py +42 -35
tests/io/test_wrappers_helpers.py +136 -0
tests/prediction_api/test_sam_onnx.py +0 -90
tests/test_fastapi_app.py +20 -16
tests/utilities/__init__.py +0 -0
tests/utilities/test_serialize.py +0 -96
wrappers/fastapi_wrapper.py +8 -4

.idea/vcs.xml CHANGED Viewed

@@ -2,5 +2,6 @@
 <project version="4">
   <component name="VcsDirectoryMappings">
     <mapping directory="" vcs="Git" />
   </component>
 </project>

 <project version="4">
   <component name="VcsDirectoryMappings">
     <mapping directory="" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/../samgis_core" vcs="Git" />
   </component>
 </project>

docs/Changelog.md CHANGED Viewed

@@ -1,5 +1,9 @@
 # Changelog
 ## Version 1.1.0
 - Added this changelog
 - specific backend branch code uses terrain providers like nextzen and MapBox Terrain-RGB v1

 # Changelog
+## Version 1.2.0
+- code refactor to separate core functionality (instance segmentation) from other code
+- updated test coverage
 ## Version 1.1.0
 - Added this changelog
 - specific backend branch code uses terrain providers like nextzen and MapBox Terrain-RGB v1

docs/conf.py CHANGED Viewed

@@ -9,9 +9,9 @@ import os
 import sys
 project = 'SamGIS'
-copyright = '2023-2024, alessandro trinca tornidor'
 author = 'alessandro trinca tornidor'
-release = '1.1.0'
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

 import sys
 project = 'SamGIS'
+copyright = '2023-now, alessandro trinca tornidor'
 author = 'alessandro trinca tornidor'
+release = '1.2.0'
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

docs/modules.rst CHANGED Viewed

@@ -5,3 +5,4 @@ samgis
    :maxdepth: 4
    samgis

    :maxdepth: 4
    samgis
+   samgis_core

docs/samgis.prediction_api.rst CHANGED Viewed

@@ -12,14 +12,6 @@ samgis.prediction\_api.predictors module
    :undoc-members:
    :show-inheritance:
-samgis.prediction\_api.sam\_onnx module
----------------------------------------
-.. automodule:: samgis.prediction_api.sam_onnx
-   :members:
-   :undoc-members:
-   :show-inheritance:
 Module contents
 ---------------

    :undoc-members:
    :show-inheritance:
 Module contents
 ---------------

docs/samgis.utilities.rst CHANGED Viewed

@@ -12,22 +12,6 @@ samgis.utilities.constants module
    :undoc-members:
    :show-inheritance:
-samgis.utilities.fastapi\_logger module
----------------------------------------
-.. automodule:: samgis.utilities.fastapi_logger
-   :members:
-   :undoc-members:
-   :show-inheritance:
-samgis.utilities.serialize module
----------------------------------
-.. automodule:: samgis.utilities.serialize
-   :members:
-   :undoc-members:
-   :show-inheritance:
 samgis.utilities.type\_hints module
 -----------------------------------
@@ -36,14 +20,6 @@ samgis.utilities.type\_hints module
    :undoc-members:
    :show-inheritance:
-samgis.utilities.utilities module
----------------------------------
-.. automodule:: samgis.utilities.utilities
-   :members:
-   :undoc-members:
-   :show-inheritance:
 Module contents
 ---------------

    :undoc-members:
    :show-inheritance:
 samgis.utilities.type\_hints module
 -----------------------------------
    :undoc-members:
    :show-inheritance:
 Module contents
 ---------------

docs/specs/openapi.yaml CHANGED Viewed

@@ -8,11 +8,12 @@ info:
     - [SamGIS documentation](https://docs.ml-trinca.tornidor.com)
     - [My blog](https://trinca.tornidor.com)
   contact:
-    url: https://www.linkedin.com/in/trincatornidor/en
   license:
     name: MIT License
     url: https://opensource.org/license/mit/
-  version: "1.1.0"
 servers:
   - url: https://localhost:8000/
 tags:

     - [SamGIS documentation](https://docs.ml-trinca.tornidor.com)
     - [My blog](https://trinca.tornidor.com)
   contact:
+    name: alessandro's website
+    url: https://trinca.tornidor.com
   license:
     name: MIT License
     url: https://opensource.org/license/mit/
+  version: "1.2.0"
 servers:
   - url: https://localhost:8000/
 tags:

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@@ -1568,13 +1568,13 @@ xmp = ["defusedxml"]
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 ]
 [package.extras]
 [[package]]
 name = "pluggy"
+version = "1.4.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
+[[package]]
+name = "samgis-core"
+version = "1.0.0"
+description = "SamGIS CORE"
+optional = false
+python-versions = ">=3.11,<4.0"
+files = [
+    {file = "samgis_core-1.0.0-py3-none-any.whl", hash = "sha256:bc468492383ff3587d248f18123b60fa185a1e68ad5ccc18ed8b79f106636572"},
+    {file = "samgis_core-1.0.0.tar.gz", hash = "sha256:2f9cb142c7950f12e9ccd23d2ce2106ddbc280a95a9e32f0fda3147884d9e6bf"},
+]
+[package.dependencies]
+bson = ">=0.5.10,<0.6.0"
+loguru = ">=0.7.2,<0.8.0"
+numpy = ">=1.26.3,<2.0.0"
+onnxruntime = ">=1.16.3,<2.0.0"
+opencv-python-headless = ">=4.9.0.80,<5.0.0.0"
+pillow = ">=10.2.0,<11.0.0"
 [[package]]
 name = "setuptools"
 version = "69.0.3"
 [[package]]
 name = "sphinx-autodoc-typehints"
+version = "1.25.3"
 description = "Type hints (PEP 484) support for the Sphinx autodoc extension"
 optional = false
 python-versions = ">=3.8"
 files = [
+    {file = "sphinx_autodoc_typehints-1.25.3-py3-none-any.whl", hash = "sha256:d3da7fa9a9761eff6ff09f8b1956ae3090a2d4f4ad54aebcade8e458d6340835"},
+    {file = "sphinx_autodoc_typehints-1.25.3.tar.gz", hash = "sha256:70db10b391acf4e772019765991d2de0ff30ec0899b9ba137706dc0b3c4835e0"},
 ]
 [package.dependencies]
 sphinx = ">=7.1.2"
 [package.extras]
+docs = ["furo (>=2023.9.10)"]
 numpy = ["nptyping (>=2.5)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "sphobjinv (>=2.3.1)", "typing-extensions (>=4.8)"]
 [[package]]
 name = "sphinx-mdinclude"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
+content-hash = "db415b27fa26250177185aa8c701f45176523e7d6f783096d9c6e46709032002"

pyproject.toml CHANGED Viewed

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "samgis"
-version = "1.1.0"
 description = "A backend for machine learning instance segmentation on geospatial data even without dedicated graphics cards."
 authors = ["alessandro trinca tornidor <[email protected]>"]
 license = "MIT license"
@@ -8,17 +8,18 @@ readme = "README.md"
 [tool.poetry.dependencies]
 bson = "^0.5.10"
-contextily = "^1.4.0"
-geopandas = "^0.14.1"
 numpy = "^1.26.2"
 onnxruntime = "^1.16.3"
 opencv-python-headless = "^4.8.1.78"
 python = "^3.11"
 python-dotenv = "^1.0.0"
 rasterio = "^1.3.9"
 requests = "^2.31.0"
-pillow = "^10.1.0"
-loguru = "^0.7.2"
 [tool.poetry.group.aws_lambda]
 optional = true

 [tool.poetry]
 name = "samgis"
+version = "1.2.0"
 description = "A backend for machine learning instance segmentation on geospatial data even without dedicated graphics cards."
 authors = ["alessandro trinca tornidor <[email protected]>"]
 license = "MIT license"
 [tool.poetry.dependencies]
 bson = "^0.5.10"
+contextily = "^1.5.0"
+geopandas = "^0.14.2"
+loguru = "^0.7.2"
 numpy = "^1.26.2"
 onnxruntime = "^1.16.3"
 opencv-python-headless = "^4.8.1.78"
+pillow = "^10.2.0"
 python = "^3.11"
 python-dotenv = "^1.0.0"
 rasterio = "^1.3.9"
 requests = "^2.31.0"
+samgis-core = "^1.0.0"
 [tool.poetry.group.aws_lambda]
 optional = true

requirements.txt DELETED Viewed

@@ -1,18 +0,0 @@
-aws-lambda-powertools
-awslambdaric
-bson
-contextily
-geopandas
-jmespath
-myst-parser
-numpy
-onnxruntime
-opencv-python-headless
-pillow
-pydantic>=2.0.3
-python-dotenv
-rasterio
-requests
-sphinx
-sphinx-autodoc-typehints
-sphinxcontrib-openapi

requirements_dev.txt DELETED Viewed

@@ -1,17 +0,0 @@
-aws-lambda-powertools
-awslambdaric
-bson
-contextily
-geopandas
-jmespath
-myst-parser
-numpy
-onnxruntime
-opencv-python-headless
-pillow
-pydantic>=2.0.3
-pytest
-pytest-cov
-python-dotenv
-rasterio
-requests

requirements_dockerfile.txt DELETED Viewed

@@ -1,14 +0,0 @@
-aws-lambda-powertools
-awslambdaric
-bson
-contextily
-geopandas
-jmespath
-numpy
-onnxruntime
-opencv-python-headless
-pillow
-pydantic>=2.0.3
-python-dotenv
-rasterio
-requests

samgis/__init__.py CHANGED Viewed

@@ -12,6 +12,6 @@ try:
     app_logger = Logger(service=SERVICE_NAME)
 except ModuleNotFoundError:
-    from samgis.utilities.fastapi_logger import setup_logging
     app_logger = setup_logging(debug=True)

     app_logger = Logger(service=SERVICE_NAME)
 except ModuleNotFoundError:
+    from samgis_core.utilities.fastapi_logger import setup_logging
     app_logger = setup_logging(debug=True)

samgis/__version__.py CHANGED Viewed

	@@ -1 +1 @@
1	- __version__ = "1.0.0"


1	+ __version__ = "1.2.0"

samgis/io/coordinates_pixel_conversion.py CHANGED Viewed

@@ -1,7 +1,9 @@
 """functions useful to convert to/from latitude-longitude coordinates to pixel image coordinates"""
 from samgis import app_logger
 from samgis.utilities.constants import TILE_SIZE, EARTH_EQUATORIAL_RADIUS
-from samgis.utilities.type_hints import ImagePixelCoordinates, tuple_float, tuple_float_any
 from samgis.utilities.type_hints import LatLngDict

 """functions useful to convert to/from latitude-longitude coordinates to pixel image coordinates"""
+from samgis_core.utilities.type_hints import tuple_float, tuple_float_any
 from samgis import app_logger
 from samgis.utilities.constants import TILE_SIZE, EARTH_EQUATORIAL_RADIUS
+from samgis.utilities.type_hints import ImagePixelCoordinates
 from samgis.utilities.type_hints import LatLngDict

samgis/io/geo_helpers.py CHANGED Viewed

@@ -2,8 +2,8 @@
 from affine import Affine
 from numpy import ndarray as np_ndarray
 from samgis import app_logger
-from samgis.utilities.type_hints import list_float, tuple_float, dict_str_int
 def load_affine_transformation_from_matrix(matrix_source_coefficients: list_float) -> Affine:

 from affine import Affine
 from numpy import ndarray as np_ndarray
+from samgis_core.utilities.type_hints import list_float, tuple_float, dict_str_int
 from samgis import app_logger
 def load_affine_transformation_from_matrix(matrix_source_coefficients: list_float) -> Affine:

samgis/io/raster_helpers.py CHANGED Viewed

@@ -87,7 +87,7 @@ def get_rgb_prediction_image(raster_cropped: ndarray, slope_cellsize: int, inver
         channel0 = raster_cropped
         channel1 = normalize_array_list(
-            [raster_cropped, slope, curvature], CHANNEL_EXAGGERATIONS_LIST, title=f"channel1_normlist")
         channel2 = curvature
         return get_rgb_image(channel0, channel1, channel2, invert_image=invert_image)
@@ -121,11 +121,11 @@ def get_rgb_image(arr_channel0: ndarray, arr_channel1: ndarray, arr_channel2: nd
         data_rgb = np.zeros((arr_channel0.shape[0], arr_channel0.shape[1], 3), dtype=np.uint8)
         app_logger.debug(f"arr_container data_rgb, type:{type(data_rgb)}, arr_shape:{data_rgb.shape}.")
         data_rgb[:, :, 0] = normalize_array(
-            arr_channel0.astype(float), high=1, norm_type="float", title=f"RGB:channel0") * 64
         data_rgb[:, :, 1] = normalize_array(
-            arr_channel1.astype(float), high=1, norm_type="float", title=f"RGB:channel1") * 128
         data_rgb[:, :, 2] = normalize_array(
-            arr_channel2.astype(float), high=1, norm_type="float", title=f"RGB:channel2") * 192
         if invert_image:
             app_logger.debug(f"data_rgb:{type(data_rgb)}, {data_rgb.dtype}.")
             data_rgb = bitwise_not(data_rgb)

         channel0 = raster_cropped
         channel1 = normalize_array_list(
+            [raster_cropped, slope, curvature], CHANNEL_EXAGGERATIONS_LIST, title="channel1_normlist")
         channel2 = curvature
         return get_rgb_image(channel0, channel1, channel2, invert_image=invert_image)
         data_rgb = np.zeros((arr_channel0.shape[0], arr_channel0.shape[1], 3), dtype=np.uint8)
         app_logger.debug(f"arr_container data_rgb, type:{type(data_rgb)}, arr_shape:{data_rgb.shape}.")
         data_rgb[:, :, 0] = normalize_array(
+            arr_channel0.astype(float), high=1, norm_type="float", title="RGB:channel0") * 64
         data_rgb[:, :, 1] = normalize_array(
+            arr_channel1.astype(float), high=1, norm_type="float", title="RGB:channel1") * 128
         data_rgb[:, :, 2] = normalize_array(
+            arr_channel2.astype(float), high=1, norm_type="float", title="RGB:channel2") * 192
         if invert_image:
             app_logger.debug(f"data_rgb:{type(data_rgb)}, {data_rgb.dtype}.")
             data_rgb = bitwise_not(data_rgb)

samgis/io/tms2geotiff.py CHANGED Viewed

@@ -1,11 +1,14 @@
 import os
 from numpy import ndarray
 from xyzservices import TileProvider
 from samgis import app_logger
 from samgis.utilities.constants import (OUTPUT_CRS_STRING, DRIVER_RASTERIO_GTIFF, N_MAX_RETRIES, N_CONNECTION, N_WAIT,
                                         ZOOM_AUTO, BOOL_USE_CACHE)
-from samgis.utilities.type_hints import tuple_ndarray_transform, tuple_float
 bool_use_cache = int(os.getenv("BOOL_USE_CACHE", BOOL_USE_CACHE))
 n_connection = int(os.getenv("N_CONNECTION", N_CONNECTION))

 import os
 from numpy import ndarray
+from samgis_core.utilities.type_hints import tuple_float
 from xyzservices import TileProvider
 from samgis import app_logger
 from samgis.utilities.constants import (OUTPUT_CRS_STRING, DRIVER_RASTERIO_GTIFF, N_MAX_RETRIES, N_CONNECTION, N_WAIT,
                                         ZOOM_AUTO, BOOL_USE_CACHE)
+from samgis.utilities.type_hints import tuple_ndarray_transform
 bool_use_cache = int(os.getenv("BOOL_USE_CACHE", BOOL_USE_CACHE))
 n_connection = int(os.getenv("N_CONNECTION", N_CONNECTION))

samgis/io/wrappers_helpers.py CHANGED Viewed

@@ -6,7 +6,7 @@ from samgis import app_logger
 from samgis.io.coordinates_pixel_conversion import get_latlng_to_pixel_coordinates
 from samgis.utilities.constants import COMPLETE_URL_TILES_MAPBOX, COMPLETE_URL_TILES_NEXTZEN, CUSTOM_RESPONSE_MESSAGES
 from samgis.utilities.type_hints import ApiRequestBody, ContentTypes, XYZTerrainProvidersNames, XYZDefaultProvidersNames
-from samgis.utilities.utilities import base64_decode
 def get_response(status: int, start_time: float, request_id: str, response_body: Dict = None) -> str:

 from samgis.io.coordinates_pixel_conversion import get_latlng_to_pixel_coordinates
 from samgis.utilities.constants import COMPLETE_URL_TILES_MAPBOX, COMPLETE_URL_TILES_NEXTZEN, CUSTOM_RESPONSE_MESSAGES
 from samgis.utilities.type_hints import ApiRequestBody, ContentTypes, XYZTerrainProvidersNames, XYZDefaultProvidersNames
+from samgis_core.utilities.utilities import base64_decode
 def get_response(status: int, start_time: float, request_id: str, response_body: Dict = None) -> str:

samgis/prediction_api/predictors.py CHANGED Viewed

@@ -1,15 +1,15 @@
 """functions using machine learning instance model(s)"""
-from numpy import array as np_array, uint8, zeros, ndarray
 from samgis import app_logger, MODEL_FOLDER
 from samgis.io.geo_helpers import get_vectorized_raster_as_geojson
 from samgis.io.raster_helpers import get_raster_terrain_rgb_like, get_rgb_prediction_image
 from samgis.io.tms2geotiff import download_extent
 from samgis.io.wrappers_helpers import check_source_type_is_terrain
-from samgis.prediction_api.sam_onnx import SegmentAnythingONNX
-from samgis.utilities.constants import (
-    MODEL_ENCODER_NAME, MODEL_DECODER_NAME, DEFAULT_URL_TILES, SLOPE_CELLSIZE, DEFAULT_INPUT_SHAPE)
-from samgis.utilities.type_hints import llist_float, dict_str_int, list_dict, tuple_ndarr_int, PIL_Image
 models_dict = {"fastsam": {"instance": None}}
@@ -53,7 +53,7 @@ def samexporter_predict(
     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)
     if check_source_type_is_terrain(source):
-        app_logger.info(f"terrain-rgb like raster: transforms it into a DEM")
         dem = get_raster_terrain_rgb_like(img, source.name)
         # set a slope cell size proportional to the image width
         slope_cellsize = int(img.shape[1] * SLOPE_CELLSIZE / DEFAULT_INPUT_SHAPE[1])
@@ -69,41 +69,3 @@ def samexporter_predict(
         "n_predictions": n_predictions,
         **get_vectorized_raster_as_geojson(mask, transform)
     }
-def get_raster_inference(
-        img: PIL_Image or ndarray, prompt: list_dict, models_instance: SegmentAnythingONNX, model_name: str
-     ) -> tuple_ndarr_int:
-    """
-    Wrapper for rasterio Affine from_gdal method
-    Args:
-        img: input PIL Image
-        prompt: list of prompt dict
-        models_instance: SegmentAnythingONNX instance model
-        model_name: model name string
-    Returns:
-        raster prediction mask, prediction number
-    """
-    np_img = np_array(img)
-    app_logger.info(f"img type {type(np_img)}, prompt:{prompt}.")
-    app_logger.debug(f"onnxruntime input shape/size (shape if PIL) {np_img.size}.")
-    try:
-        app_logger.debug(f"onnxruntime input shape (NUMPY) {np_img.shape}.")
-    except Exception as e_shape:
-        app_logger.error(f"e_shape:{e_shape}.")
-    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_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 = zeros((inference_out.shape[2], inference_out.shape[3]), dtype=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_inference_out

 """functions using machine learning instance model(s)"""
 from samgis import app_logger, MODEL_FOLDER
 from samgis.io.geo_helpers import get_vectorized_raster_as_geojson
 from samgis.io.raster_helpers import get_raster_terrain_rgb_like, get_rgb_prediction_image
 from samgis.io.tms2geotiff import download_extent
 from samgis.io.wrappers_helpers import check_source_type_is_terrain
+from samgis.utilities.constants import DEFAULT_URL_TILES, SLOPE_CELLSIZE
+from samgis_core.prediction_api.sam_onnx import SegmentAnythingONNX
+from samgis_core.prediction_api.sam_onnx import get_raster_inference
+from samgis_core.utilities.constants import MODEL_ENCODER_NAME, MODEL_DECODER_NAME, DEFAULT_INPUT_SHAPE
+from samgis_core.utilities.type_hints import llist_float, dict_str_int, list_dict
 models_dict = {"fastsam": {"instance": None}}
     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)
     if check_source_type_is_terrain(source):
+        app_logger.info("terrain-rgb like raster: transforms it into a DEM")
         dem = get_raster_terrain_rgb_like(img, source.name)
         # set a slope cell size proportional to the image width
         slope_cellsize = int(img.shape[1] * SLOPE_CELLSIZE / DEFAULT_INPUT_SHAPE[1])
         "n_predictions": n_predictions,
         **get_vectorized_raster_as_geojson(mask, transform)
     }

samgis/prediction_api/sam_onnx.py DELETED Viewed

@@ -1,246 +0,0 @@
-"""
-Define a machine learning model executed by ONNX Runtime (https://ai/)
-for Segment Anything (https://segment-anything.com).
-Modified from https://github.com/vietanhdev/samexporter/
-Copyright (c) 2023 Viet Anh Nguyen
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-"""
-from copy import deepcopy
-from numpy import array as np_array, concatenate, float32, linalg, matmul, ndarray, ones, zeros
-from cv2 import INTER_LINEAR, warpAffine
-from onnxruntime import get_available_providers, InferenceSession
-from samgis import app_logger
-from samgis.utilities.constants import DEFAULT_INPUT_SHAPE
-class SegmentAnythingONNX:
-    """Segmentation model using SegmentAnything"""
-    def __init__(self, encoder_model_path, decoder_model_path) -> None:
-        self.target_size = DEFAULT_INPUT_SHAPE[1]
-        self.input_size = DEFAULT_INPUT_SHAPE
-        # Load models
-        providers = get_available_providers()
-        # Pop TensorRT Runtime due to crashing issues
-        # TODO: Add back when TensorRT backend is stable
-        providers = [p for p in providers if p != "TensorrtExecutionProvider"]
-        if providers:
-            app_logger.info(
-                "Available providers for ONNXRuntime: %s", ", ".join(providers)
-            )
-        else:
-            app_logger.warning("No available providers for ONNXRuntime")
-        self.encoder_session = InferenceSession(
-            encoder_model_path, providers=providers
-        )
-        self.encoder_input_name = self.encoder_session.get_inputs()[0].name
-        self.decoder_session = InferenceSession(
-            decoder_model_path, providers=providers
-        )
-    @staticmethod
-    def get_input_points(prompt):
-        """Get input points"""
-        points = []
-        labels = []
-        for mark in prompt:
-            if mark["type"] == "point":
-                points.append(mark["data"])
-                labels.append(mark["label"])
-            elif mark["type"] == "rectangle":
-                points.append([mark["data"][0], mark["data"][1]])  # top left
-                points.append(
-                    [mark["data"][2], mark["data"][3]]
-                )  # bottom right
-                labels.append(2)
-                labels.append(3)
-        points, labels = np_array(points), np_array(labels)
-        return points, labels
-    def run_encoder(self, encoder_inputs):
-        """Run encoder"""
-        output = self.encoder_session.run(None, encoder_inputs)
-        image_embedding = output[0]
-        return image_embedding
-    @staticmethod
-    def get_preprocess_shape(old_h: int, old_w: int, long_side_length: int):
-        """
-        Compute the output size given input size and target long side length.
-        """
-        scale = long_side_length * 1.0 / max(old_h, old_w)
-        new_h, new_w = old_h * scale, old_w * scale
-        new_w = int(new_w + 0.5)
-        new_h = int(new_h + 0.5)
-        return new_h, new_w
-    def apply_coords(self, coords: ndarray, original_size, target_length):
-        """
-        Expects a numpy np_array of length 2 in the final dimension. Requires the
-        original image size in (H, W) format.
-        """
-        old_h, old_w = original_size
-        new_h, new_w = self.get_preprocess_shape(
-            original_size[0], original_size[1], target_length
-        )
-        coords = deepcopy(coords).astype(float)
-        coords[..., 0] = coords[..., 0] * (new_w / old_w)
-        coords[..., 1] = coords[..., 1] * (new_h / old_h)
-        return coords
-    def run_decoder(
-        self, image_embedding, original_size, transform_matrix, prompt
-    ):
-        """Run decoder"""
-        input_points, input_labels = self.get_input_points(prompt)
-        # Add a batch index, concatenate a padding point, and transform.
-        onnx_coord = concatenate(
-            [input_points, np_array([[0.0, 0.0]])], axis=0
-        )[None, :, :]
-        onnx_label = concatenate([input_labels, np_array([-1])], axis=0)[
-            None, :
-        ].astype(float32)
-        onnx_coord = self.apply_coords(
-            onnx_coord, self.input_size, self.target_size
-        ).astype(float32)
-        # Apply the transformation matrix to the coordinates.
-        onnx_coord = concatenate(
-            [
-                onnx_coord,
-                ones((1, onnx_coord.shape[1], 1), dtype=float32),
-            ],
-            axis=2,
-        )
-        onnx_coord = matmul(onnx_coord, transform_matrix.T)
-        onnx_coord = onnx_coord[:, :, :2].astype(float32)
-        # Create an empty mask input and an indicator for no mask.
-        onnx_mask_input = zeros((1, 1, 256, 256), dtype=float32)
-        onnx_has_mask_input = zeros(1, dtype=float32)
-        decoder_inputs = {
-            "image_embeddings": image_embedding,
-            "point_coords": onnx_coord,
-            "point_labels": onnx_label,
-            "mask_input": onnx_mask_input,
-            "has_mask_input": onnx_has_mask_input,
-            "orig_im_size": np_array(self.input_size, dtype=float32),
-        }
-        masks, _, _ = self.decoder_session.run(None, decoder_inputs)
-        # Transform the masks back to the original image size.
-        inv_transform_matrix = linalg.inv(transform_matrix)
-        transformed_masks = self.transform_masks(
-            masks, original_size, inv_transform_matrix
-        )
-        return transformed_masks
-    @staticmethod
-    def transform_masks(masks, original_size, transform_matrix):
-        """Transform masks
-        Transform the masks back to the original image size.
-        """
-        output_masks = []
-        for batch in range(masks.shape[0]):
-            batch_masks = []
-            for mask_id in range(masks.shape[1]):
-                mask = masks[batch, mask_id]
-                try:
-                    try:
-                        app_logger.debug(f"mask_shape transform_masks:{mask.shape}, dtype:{mask.dtype}.")
-                    except Exception as e_mask_shape_transform_masks:
-                        app_logger.error(f"e_mask_shape_transform_masks:{e_mask_shape_transform_masks}.")
-                    mask = warpAffine(
-                        mask,
-                        transform_matrix[:2],
-                        (original_size[1], original_size[0]),
-                        flags=INTER_LINEAR,
-                    )
-                except Exception as e_warp_affine1:
-                    app_logger.error(f"e_warp_affine1 mask shape:{mask.shape}, dtype:{mask.dtype}.")
-                    app_logger.error(f"e_warp_affine1 transform_matrix:{transform_matrix}, [:2] {transform_matrix[:2]}.")
-                    app_logger.error(f"e_warp_affine1 original_size:{original_size}.")
-                    raise e_warp_affine1
-                batch_masks.append(mask)
-            output_masks.append(batch_masks)
-        return np_array(output_masks)
-    def encode(self, cv_image):
-        """
-        Calculate embedding and metadata for a single image.
-        """
-        original_size = cv_image.shape[:2]
-        # Calculate a transformation matrix to convert to self.input_size
-        scale_x = self.input_size[1] / cv_image.shape[1]
-        scale_y = self.input_size[0] / cv_image.shape[0]
-        scale = min(scale_x, scale_y)
-        transform_matrix = np_array(
-            [
-                [scale, 0, 0],
-                [0, scale, 0],
-                [0, 0, 1],
-            ]
-        )
-        try:
-            cv_image = warpAffine(
-                cv_image,
-                transform_matrix[:2],
-                (self.input_size[1], self.input_size[0]),
-                flags=INTER_LINEAR,
-            )
-        except Exception as e_warp_affine2:
-            app_logger.error(f"e_warp_affine2:{e_warp_affine2}.")
-            np_cv_image = np_array(cv_image)
-            app_logger.error(f"e_warp_affine2 cv_image shape:{np_cv_image.shape}, dtype:{np_cv_image.dtype}.")
-            app_logger.error(f"e_warp_affine2 transform_matrix:{transform_matrix}, [:2] {transform_matrix[:2]}")
-            app_logger.error(f"e_warp_affine2 self.input_size:{self.input_size}.")
-            raise e_warp_affine2
-        encoder_inputs = {
-            self.encoder_input_name: cv_image.astype(float32),
-        }
-        image_embedding = self.run_encoder(encoder_inputs)
-        return {
-            "image_embedding": image_embedding,
-            "original_size": original_size,
-            "transform_matrix": transform_matrix,
-        }
-    def predict_masks(self, embedding, prompt):
-        """
-        Predict masks for a single image.
-        """
-        masks = self.run_decoder(
-            embedding["image_embedding"],
-            embedding["original_size"],
-            embedding["transform_matrix"],
-            prompt,
-        )
-        return masks

samgis/utilities/constants.py CHANGED Viewed

@@ -9,8 +9,6 @@ CUSTOM_RESPONSE_MESSAGES = {
     422: "Missing required parameter",
     500: "Internal server error"
 }
-MODEL_ENCODER_NAME = "mobile_sam.encoder.onnx"
-MODEL_DECODER_NAME = "sam_vit_h_4b8939.decoder.onnx"
 TILE_SIZE = 256
 EARTH_EQUATORIAL_RADIUS = 6378137.0
 WKT_3857 = 'PROJCS["WGS 84 / Pseudo-Mercator",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,'
@@ -39,5 +37,4 @@ DOMAIN_URL_TILES_NEXTZEN = "s3.amazonaws.com"
 RELATIVE_URL_TILES_NEXTZEN = "elevation-tiles-prod/terrarium/{z}/{x}/{y}.png"  # "terrarium/{z}/{x}/{y}.png"
 COMPLETE_URL_TILES_NEXTZEN = f"https://{DOMAIN_URL_TILES_NEXTZEN}/{RELATIVE_URL_TILES_NEXTZEN}"
 CHANNEL_EXAGGERATIONS_LIST = [2.5, 1.1, 2.0]
-DEFAULT_INPUT_SHAPE = 684, 1024
 SLOPE_CELLSIZE = 61

     422: "Missing required parameter",
     500: "Internal server error"
 }
 TILE_SIZE = 256
 EARTH_EQUATORIAL_RADIUS = 6378137.0
 WKT_3857 = 'PROJCS["WGS 84 / Pseudo-Mercator",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,'
 RELATIVE_URL_TILES_NEXTZEN = "elevation-tiles-prod/terrarium/{z}/{x}/{y}.png"  # "terrarium/{z}/{x}/{y}.png"
 COMPLETE_URL_TILES_NEXTZEN = f"https://{DOMAIN_URL_TILES_NEXTZEN}/{RELATIVE_URL_TILES_NEXTZEN}"
 CHANNEL_EXAGGERATIONS_LIST = [2.5, 1.1, 2.0]
 SLOPE_CELLSIZE = 61

samgis/utilities/fastapi_logger.py DELETED Viewed

@@ -1,24 +0,0 @@
-import loguru
-def setup_logging(debug: bool = False, formatter: str = "{time} - {level} - ({extra[request_id]}) {message} "
-                  ) -> loguru.logger:
-    """
-    Create a logging instance with log string formatter.
-    Args:
-        debug: logging debug argument
-        formatter: log string formatter
-    Returns:
-        Logger
-    """
-    import sys
-    logger = loguru.logger
-    logger.remove()
-    level_logger = "DEBUG" if debug else "INFO"
-    logger.add(sys.stdout, format=formatter, level=level_logger)
-    logger.info(f"type_logger:{type(logger)}, logger:{logger}.")
-    return logger

samgis/utilities/serialize.py DELETED Viewed

@@ -1,84 +0,0 @@
-"""Serialize objects"""
-from typing import Mapping
-from samgis import app_logger
-from samgis.utilities.type_hints import dict_str, dict_str_any
-def serialize(obj: any, include_none: bool = False):
-    """
-    Return the input object into a serializable one
-    Args:
-        obj: Object to serialize
-        include_none: bool to indicate if include also keys with None values during dict serialization
-    Returns:
-        serialized object
-    """
-    return _serialize(obj, include_none)
-def _serialize(obj: any, include_none: bool):
-    from numpy import ndarray as np_ndarray, floating as np_floating, integer as np_integer
-    primitive = (int, float, str, bool)
-    # print(type(obj))
-    try:
-        if obj is None:
-            return None
-        elif isinstance(obj, np_integer):
-            return int(obj)
-        elif isinstance(obj, np_floating):
-            return float(obj)
-        elif isinstance(obj, np_ndarray):
-            return obj.tolist()
-        elif isinstance(obj, primitive):
-            return obj
-        elif type(obj) is list:
-            return _serialize_list(obj, include_none)
-        elif type(obj) is tuple:
-            return list(obj)
-        elif type(obj) is bytes:
-            return _serialize_bytes(obj)
-        elif isinstance(obj, Exception):
-            return _serialize_exception(obj)
-        # elif isinstance(obj, object):
-        #     return _serialize_object(obj, include_none)
-        else:
-            return _serialize_object(obj, include_none)
-    except Exception as e_serialize:
-        app_logger.error(f"e_serialize::{e_serialize}, type_obj:{type(obj)}, obj:{obj}.")
-        return f"object_name:{str(obj)}__object_type_str:{str(type(obj))}."
-def _serialize_object(obj: Mapping[any, object], include_none: bool) -> dict[any]:
-    from bson import ObjectId
-    res = {}
-    if type(obj) is not dict:
-        keys = [i for i in obj.__dict__.keys() if (getattr(obj, i) is not None) or include_none]
-    else:
-        keys = [i for i in obj.keys() if (obj[i] is not None) or include_none]
-    for key in keys:
-        if type(obj) is not dict:
-            res[key] = _serialize(getattr(obj, key), include_none)
-        elif isinstance(obj[key], ObjectId):
-            continue
-        else:
-            res[key] = _serialize(obj[key], include_none)
-    return res
-def _serialize_list(ls: list, include_none: bool) -> list:
-    return [_serialize(elem, include_none) for elem in ls]
-def _serialize_bytes(b: bytes) -> dict_str:
-    import base64
-    encoded = base64.b64encode(b)
-    return {"value": encoded.decode('ascii'), "type": "bytes"}
-def _serialize_exception(e: Exception) -> dict_str_any:
-    return {"msg": str(e), "type": str(type(e)), **e.__dict__}

samgis/utilities/type_hints.py CHANGED Viewed

@@ -2,24 +2,11 @@
 from enum import IntEnum, Enum, StrEnum
 from typing import TypedDict
-from PIL.Image import Image
 from affine import Affine
 from numpy import ndarray
 from pydantic import BaseModel
-dict_str_int = dict[str, int]
-dict_str = dict[str]
-dict_str_any = dict[str, any]
-list_dict = list[dict]
-list_float = list[float]
-list_int = list[int]
-tuple_int = tuple[int]
-tuple_ndarr_int = tuple[ndarray, int]
-llist_float = list[list_float]
-tuple_float = tuple[float]
-tuple_float_any = tuple[float, any]
-PIL_Image = Image
 tuple_ndarray_transform = tuple[ndarray, Affine]

 from enum import IntEnum, Enum, StrEnum
 from typing import TypedDict
 from affine import Affine
 from numpy import ndarray
 from pydantic import BaseModel
 tuple_ndarray_transform = tuple[ndarray, Affine]

samgis/utilities/utilities.py DELETED Viewed

@@ -1,92 +0,0 @@
-"""Various utilities (logger, time benchmark, args dump, numerical and stats info)"""
-from samgis import app_logger
-from samgis.utilities.serialize import serialize
-def _prepare_base64_input(sb):
-    if isinstance(sb, str):
-        # If there's any unicode here, an exception will be thrown and the function will return false
-        return bytes(sb, 'ascii')
-    elif isinstance(sb, bytes):
-        return sb
-    raise ValueError("Argument must be string or bytes")
-def _is_base64(sb: str or bytes):
-    import base64
-    try:
-        sb_bytes = _prepare_base64_input(sb)
-        decoded = base64.b64decode(sb_bytes, validate=True)
-        return base64.b64encode(decoded).decode("utf-8") == sb_bytes.decode("utf-8")
-    except ValueError:
-        return False
-def base64_decode(s):
-    """
-    Decode base64 strings
-    Args:
-        s: input string
-    Returns:
-        decoded string
-    """
-    import base64
-    if isinstance(s, str) and _is_base64(s):
-        return base64.b64decode(s, validate=True).decode("utf-8")
-    return s
-def base64_encode(sb: str or bytes) -> bytes:
-    """
-    Encode input strings or bytes as base64
-    Args:
-        sb: input string or bytes
-    Returns:
-        base64 encoded bytes
-    """
-    import base64
-    sb_bytes = _prepare_base64_input(sb)
-    return base64.b64encode(sb_bytes)
-def hash_calculate(arr) -> str or bytes:
-    """
-    Return computed hash from input variable (typically a numpy array).
-    Args:
-        arr: input variable
-    Returns:
-        computed hash from input variable
-    """
-    from hashlib import sha256
-    from base64 import b64encode
-    from numpy import ndarray as np_ndarray
-    if isinstance(arr, np_ndarray):
-        hash_fn = sha256(arr.data)
-    elif isinstance(arr, dict):
-        import json
-        serialized = serialize(arr)
-        variable_to_hash = json.dumps(serialized, sort_keys=True).encode('utf-8')
-        hash_fn = sha256(variable_to_hash)
-    elif isinstance(arr, str):
-        try:
-            hash_fn = sha256(arr)
-        except TypeError:
-            app_logger.warning(f"TypeError, re-try encoding arg:{arr},type:{type(arr)}.")
-            hash_fn = sha256(arr.encode('utf-8'))
-    elif isinstance(arr, bytes):
-        hash_fn = sha256(arr)
-    else:
-        raise ValueError(f"variable 'arr':{arr} not yet handled.")
-    return b64encode(hash_fn.digest())

tests/io/test_coordinates_pixel_conversion.py CHANGED Viewed

@@ -1,37 +1,10 @@
 import json
-from samgis.io.coordinates_pixel_conversion import _get_latlng2pixel_projection, _get_point_latlng_to_pixel_coordinates, \
-    get_latlng_to_pixel_coordinates
 from samgis.utilities.type_hints import LatLngDict
 from tests import TEST_EVENTS_FOLDER
-def test_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 = LatLngDict.model_validate(current_input["latlng"])
-            output = _get_latlng2pixel_projection(latlng_input)
-            assert output == input_output["output"]
-def test_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 = LatLngDict.model_validate(current_input["latlng"])
-            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():
     name_fn = "get_latlng_to_pixel_coordinates"

 import json
+from samgis.io.coordinates_pixel_conversion import get_latlng_to_pixel_coordinates
 from samgis.utilities.type_hints import LatLngDict
 from tests import TEST_EVENTS_FOLDER
 def test_get_latlng_to_pixel_coordinates():
     name_fn = "get_latlng_to_pixel_coordinates"

tests/io/test_geo_helpers.py CHANGED Viewed

@@ -1,6 +1,5 @@
 import json
 import unittest
 import numpy as np
 import shapely

 import json
 import unittest
 import numpy as np
 import shapely

tests/io/test_lambda_helpers.py DELETED Viewed

@@ -1,133 +0,0 @@
-import json
-import time
-from http import HTTPStatus
-from unittest.mock import patch
-from samgis.io import wrappers_helpers
-from samgis.io.wrappers_helpers import get_parsed_bbox_points, get_parsed_request_body, get_response
-from samgis.utilities.type_hints import ApiRequestBody
-from samgis.utilities import utilities
-from tests import TEST_EVENTS_FOLDER
-@patch.object(time, "time")
-def test_get_response(time_mocked):
-    time_diff = 108
-    end_run = 1000
-    time_mocked.return_value = end_run
-    start_time = end_run - time_diff
-    aws_request_id = "test_invoke_id"
-    with open(TEST_EVENTS_FOLDER / "get_response.json") as tst_json:
-        inputs_outputs = json.load(tst_json)
-    response_type = "200"
-    body_response = inputs_outputs[response_type]["input"]
-    output = get_response(HTTPStatus.OK.value, start_time, aws_request_id, body_response)
-    assert json.loads(output) == inputs_outputs[response_type]["output"]
-    response_type = "400"
-    response_400 = get_response(HTTPStatus.BAD_REQUEST.value, start_time, aws_request_id, {})
-    assert response_400 == inputs_outputs[response_type]["output"]
-    response_type = "422"
-    response_422 = get_response(HTTPStatus.UNPROCESSABLE_ENTITY.value, start_time, aws_request_id, {})
-    assert response_422 == inputs_outputs[response_type]["output"]
-    response_type = "500"
-    response_500 = get_response(HTTPStatus.INTERNAL_SERVER_ERROR.value, start_time, aws_request_id, {})
-    assert response_500 == inputs_outputs[response_type]["output"]
-def test_get_parsed_bbox_points():
-    with open(TEST_EVENTS_FOLDER / "get_parsed_bbox_prompts_single_point.json") as tst_json:
-        inputs_outputs = json.load(tst_json)
-        for k, input_output in inputs_outputs.items():
-            print(f"k:{k}.")
-            raw_body = get_parsed_request_body(**input_output["input"])
-            output = get_parsed_bbox_points(raw_body)
-            assert output == input_output["output"]
-def test_get_parsed_bbox_other_inputs():
-    for json_filename in ["single_rectangle", "multi_prompt"]:
-        with open(TEST_EVENTS_FOLDER / f"get_parsed_bbox_prompts_{json_filename}.json") as tst_json:
-            inputs_outputs = json.load(tst_json)
-            parsed_input = ApiRequestBody.model_validate(inputs_outputs["input"])
-            output = get_parsed_bbox_points(parsed_input)
-            assert output == inputs_outputs["output"]
-def test_get_parsed_request_body():
-    input_event = {
-        "event": {
-            "bbox": {
-                "ne": {"lat": 38.03932961278458, "lng": 15.36808069832851},
-                "sw": {"lat": 37.455509218936974, "lng": 14.632807441554068}
-            },
-            "prompt": [{"type": "point", "data": {"lat": 37.0, "lng": 15.0}, "label": 0}],
-            "zoom": 10, "source_type": "OpenStreetMap.Mapnik", "debug": True
-        }
-    }
-    expected_output_dict = {
-        "bbox": {
-            "ne": {"lat": 38.03932961278458, "lng": 15.36808069832851},
-            "sw": {"lat": 37.455509218936974, "lng": 14.632807441554068}
-        },
-        "prompt": [{"type": "point", "data": {"lat": 37.0, "lng": 15.0}, "label": 0}],
-        "zoom": 10, "source_type": "OpenStreetMap.Mapnik", "debug": True
-    }
-    output = get_parsed_request_body(input_event["event"])
-    assert output == ApiRequestBody.model_validate(input_event["event"])
-    input_event_str = json.dumps(input_event["event"])
-    output = get_parsed_request_body(input_event_str)
-    assert output == ApiRequestBody.model_validate(expected_output_dict)
-    event = {"body": utilities.base64_encode(input_event_str).decode("utf-8")}
-    output = get_parsed_request_body(event)
-    assert output == ApiRequestBody.model_validate(expected_output_dict)
-@patch.object(wrappers_helpers, "providers")
-def test_get_url_tile(providers_mocked):
-    import xyzservices
-    from samgis.io.wrappers_helpers import get_url_tile
-    from tests import LOCAL_URL_TILE
-    local_tile_provider = xyzservices.TileProvider(name="local_tile_provider", url=LOCAL_URL_TILE, attribution="")
-    expected_output = {'name': 'local_tile_provider', 'url': LOCAL_URL_TILE, 'attribution': ''}
-    providers_mocked.query_name.return_value = local_tile_provider
-    assert get_url_tile("OpenStreetMap") == expected_output
-    local_url = 'http://localhost:8000/{parameter}/{z}/{x}/{y}.png'
-    local_tile_provider = xyzservices.TileProvider(
-        name="local_tile_provider_param", url=local_url, attribution="", parameter="lamda_handler"
-    )
-    providers_mocked.query_name.return_value = local_tile_provider
-    assert get_url_tile("OpenStreetMap.HOT") == {
-        "parameter": "lamda_handler", 'name': 'local_tile_provider_param', 'url': local_url, 'attribution': ''
-    }
-def test_get_url_tile_real():
-    from samgis.io.wrappers_helpers import get_url_tile
-    assert get_url_tile("OpenStreetMap") == {
-        'url': 'https://tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19,
-        'html_attribution': '&copy; <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',
-        'attribution': '(C) OpenStreetMap contributors',
-        'name': 'OpenStreetMap.Mapnik'}
-    html_attribution_hot = '&copy; <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors, '
-    html_attribution_hot += 'Tiles style by <a href="https://www.hotosm.org/" target="_blank">Humanitarian '
-    html_attribution_hot += 'OpenStreetMap Team</a> hosted by <a href="https://openstreetmap.fr/" target="_blank">'
-    html_attribution_hot += 'OpenStreetMap France</a>'
-    attribution_hot = '(C) OpenStreetMap contributors, Tiles style by Humanitarian OpenStreetMap Team hosted by '
-    attribution_hot += 'OpenStreetMap France'
-    assert get_url_tile("OpenStreetMap.HOT") == {
-        'url': 'https://{s}.tile.openstreetmap.fr/hot/{z}/{x}/{y}.png', 'max_zoom': 19,
-        'html_attribution': html_attribution_hot, 'attribution': attribution_hot, 'name': 'OpenStreetMap.HOT'
-    }

tests/io/test_raster_helpers.py CHANGED Viewed

@@ -1,9 +1,9 @@
-import numpy as np
 import unittest
 from unittest.mock import patch
 from samgis.io import raster_helpers
-from samgis.utilities.utilities import hash_calculate
 def get_three_channels(size=5, param1=1000, param2=3, param3=-88):

 import unittest
 from unittest.mock import patch
+import numpy as np
+from samgis_core.utilities.utilities import hash_calculate
 from samgis.io import raster_helpers
 def get_three_channels(size=5, param1=1000, param2=3, param3=-88):

tests/io/test_tms2geotiff.py CHANGED Viewed

@@ -1,40 +1,41 @@
 import unittest
 import numpy as np
 from samgis import app_logger
 from samgis.io.tms2geotiff import download_extent
-from samgis.utilities.utilities import hash_calculate
 from tests import LOCAL_URL_TILE, TEST_EVENTS_FOLDER
 input_bbox = [[39.036252959636606, 15.040283203125002], [38.302869955150044, 13.634033203125002]]
 class TestTms2geotiff(unittest.TestCase):
-    def test_download_extent_simple_source(self):
-        from rasterio import Affine
-        from xyzservices import TileProvider
-        from tests.local_tiles_http_server import LocalTilesHttpServer
-        listen_port = 8000
-        with LocalTilesHttpServer.http_server("localhost", listen_port, directory=TEST_EVENTS_FOLDER):
-            pt0, pt1 = input_bbox
-            zoom = 10
-            n_lat = pt0[0]
-            e_lng = pt0[1]
-            s_lat = pt1[0]
-            w_lng = pt1[1]
-            source = TileProvider(name="local_tile_provider", url=LOCAL_URL_TILE, attribution="")
-            img, matrix = download_extent(w=w_lng, s=s_lat, e=e_lng, n=n_lat, zoom=zoom, source=source)
-            app_logger.info(f"# DOWNLOAD ENDED, shape: {img.shape} #")
-            np_img = np.ascontiguousarray(img)
-            output_hash = hash_calculate(np_img)
-            assert output_hash == b'UmbkwbPJpRT1XXcLnLUapUDP320w7YhS/AmT3H7u+b4='
-            assert Affine.to_gdal(matrix) == (
-                1517657.1966021745, 152.8740565703525, 0.0, 4726942.266183584, 0.0, -152.87405657034955)
     def test_download_extent_source_with_parameter(self):
         from rasterio import Affine
@@ -52,16 +53,22 @@ class TestTms2geotiff(unittest.TestCase):
             s_lat = pt1[0]
             w_lng = pt1[1]
-            local_url_tile2 = "http://localhost:8000/{parameter}/{z}/{x}/{y}.png"
-            source = TileProvider(
-                name="local_tile_provider", url=local_url_tile2, attribution="", parameter="lambda_handler")
-            img, matrix = download_extent(w=w_lng, s=s_lat, e=e_lng, n=n_lat, zoom=zoom, source=source)
-            app_logger.info(f"# DOWNLOAD ENDED, shape: {img.shape} #")
-            np_img = np.ascontiguousarray(img)
-            output_hash = hash_calculate(np_img)
-            assert output_hash == b'UmbkwbPJpRT1XXcLnLUapUDP320w7YhS/AmT3H7u+b4='
-            assert Affine.to_gdal(matrix) == (
-                1517657.1966021745, 152.8740565703525, 0.0, 4726942.266183584, 0.0, -152.87405657034955)
     def test_download_extent_source_with_parameter_key_error(self):
         from xyzservices import TileProvider

 import unittest
 import numpy as np
+from samgis_core.utilities.utilities import hash_calculate
 from samgis import app_logger
 from samgis.io.tms2geotiff import download_extent
 from tests import LOCAL_URL_TILE, TEST_EVENTS_FOLDER
 input_bbox = [[39.036252959636606, 15.040283203125002], [38.302869955150044, 13.634033203125002]]
 class TestTms2geotiff(unittest.TestCase):
+    # def test_download_extent_simple_source(self):
+    #     from rasterio import Affine
+    #     from xyzservices import TileProvider
+    #     from tests.local_tiles_http_server import LocalTilesHttpServer
+    #
+    #     listen_port = 8000
+    #
+    #     with LocalTilesHttpServer.http_server("localhost", listen_port, directory=TEST_EVENTS_FOLDER):
+    #         pt0, pt1 = input_bbox
+    #         zoom = 10
+    #
+    #         n_lat = pt0[0]
+    #         e_lng = pt0[1]
+    #         s_lat = pt1[0]
+    #         w_lng = pt1[1]
+    #
+    #         source = TileProvider(name="local_tile_provider", url=LOCAL_URL_TILE, attribution="")
+    #         img, matrix = download_extent(w=w_lng, s=s_lat, e=e_lng, n=n_lat, zoom=zoom, source=source)
+    #         app_logger.info(f"# DOWNLOAD ENDED, shape: {img.shape} #")
+    #         np_img = np.ascontiguousarray(img)
+    #         output_hash = hash_calculate(np_img)
+    #         assert output_hash == b'UmbkwbPJpRT1XXcLnLUapUDP320w7YhS/AmT3H7u+b4='
+    #         assert Affine.to_gdal(matrix) == (
+    #             1517657.1966021745, 152.8740565703525, 0.0, 4726942.266183584, 0.0, -152.87405657034955)
     def test_download_extent_source_with_parameter(self):
         from rasterio import Affine
             s_lat = pt1[0]
             w_lng = pt1[1]
+            local_url = "http://localhost:8000/{parameter}/{z}/{x}/{y}.png"
+            download_extent_args_no_parameter = {"name": "local_tile_provider", "url": LOCAL_URL_TILE, "attribution": ""}
+            download_extent_args = {
+                "no_parameter": download_extent_args_no_parameter,
+                "with_parameter": {"url": local_url, "parameter": "lambda_handler", **download_extent_args_no_parameter}
+            }
+            for _args_names, _args in download_extent_args.items():
+                app_logger.info(f"args_names:{_args_names}.")
+                source = TileProvider(**_args)
+                img, matrix = download_extent(w=w_lng, s=s_lat, e=e_lng, n=n_lat, zoom=zoom, source=source)
+                app_logger.info(f"# DOWNLOAD ENDED, shape: {img.shape} #")
+                np_img = np.ascontiguousarray(img)
+                output_hash = hash_calculate(np_img)
+                assert output_hash == b'UmbkwbPJpRT1XXcLnLUapUDP320w7YhS/AmT3H7u+b4='
+                assert Affine.to_gdal(matrix) == (
+                    1517657.1966021745, 152.8740565703525, 0.0, 4726942.266183584, 0.0, -152.87405657034955)
     def test_download_extent_source_with_parameter_key_error(self):
         from xyzservices import TileProvider

tests/io/test_wrappers_helpers.py ADDED Viewed

	@@ -0,0 +1,136 @@

+import json
+import time
+import unittest
+from http import HTTPStatus
+from unittest.mock import patch
+from samgis.io import wrappers_helpers
+from samgis.io.wrappers_helpers import get_parsed_bbox_points, get_parsed_request_body, get_response
+from samgis.utilities.type_hints import ApiRequestBody
+from tests import TEST_EVENTS_FOLDER
+class WrappersHelpersTest(unittest.TestCase):
+    @patch.object(time, "time")
+    def test_get_response(self, time_mocked):
+        time_diff = 108
+        end_run = 1000
+        time_mocked.return_value = end_run
+        start_time = end_run - time_diff
+        aws_request_id = "test_invoke_id"
+        with open(TEST_EVENTS_FOLDER / "get_response.json") as tst_json:
+            inputs_outputs = json.load(tst_json)
+        response_type = "200"
+        body_response = inputs_outputs[response_type]["input"]
+        output = get_response(HTTPStatus.OK.value, start_time, aws_request_id, body_response)
+        assert json.loads(output) == inputs_outputs[response_type]["output"]
+        response_type = "400"
+        response_400 = get_response(HTTPStatus.BAD_REQUEST.value, start_time, aws_request_id, {})
+        assert response_400 == inputs_outputs[response_type]["output"]
+        response_type = "422"
+        response_422 = get_response(HTTPStatus.UNPROCESSABLE_ENTITY.value, start_time, aws_request_id, {})
+        assert response_422 == inputs_outputs[response_type]["output"]
+        response_type = "500"
+        response_500 = get_response(HTTPStatus.INTERNAL_SERVER_ERROR.value, start_time, aws_request_id, {})
+        assert response_500 == inputs_outputs[response_type]["output"]
+    @staticmethod
+    def test_get_parsed_bbox_points():
+        with open(TEST_EVENTS_FOLDER / "get_parsed_bbox_prompts_single_point.json") as tst_json:
+            inputs_outputs = json.load(tst_json)
+            for k, input_output in inputs_outputs.items():
+                print(f"k:{k}.")
+                raw_body = get_parsed_request_body(**input_output["input"])
+                output = get_parsed_bbox_points(raw_body)
+                assert output == input_output["output"]
+    @staticmethod
+    def test_get_parsed_bbox_other_inputs():
+        for json_filename in ["single_rectangle", "multi_prompt"]:
+            with open(TEST_EVENTS_FOLDER / f"get_parsed_bbox_prompts_{json_filename}.json") as tst_json:
+                inputs_outputs = json.load(tst_json)
+                parsed_input = ApiRequestBody.model_validate(inputs_outputs["input"])
+                output = get_parsed_bbox_points(parsed_input)
+                assert output == inputs_outputs["output"]
+    @staticmethod
+    def test_get_parsed_request_body():
+        from samgis_core.utilities.utilities import base64_encode
+        input_event = {
+            "event": {
+                "bbox": {
+                    "ne": {"lat": 38.03932961278458, "lng": 15.36808069832851},
+                    "sw": {"lat": 37.455509218936974, "lng": 14.632807441554068}
+                },
+                "prompt": [{"type": "point", "data": {"lat": 37.0, "lng": 15.0}, "label": 0}],
+                "zoom": 10, "source_type": "OpenStreetMap.Mapnik", "debug": True
+            }
+        }
+        expected_output_dict = {
+            "bbox": {
+                "ne": {"lat": 38.03932961278458, "lng": 15.36808069832851},
+                "sw": {"lat": 37.455509218936974, "lng": 14.632807441554068}
+            },
+            "prompt": [{"type": "point", "data": {"lat": 37.0, "lng": 15.0}, "label": 0}],
+            "zoom": 10, "source_type": "OpenStreetMap.Mapnik", "debug": True
+        }
+        output = get_parsed_request_body(input_event["event"])
+        assert output == ApiRequestBody.model_validate(input_event["event"])
+        input_event_str = json.dumps(input_event["event"])
+        output = get_parsed_request_body(input_event_str)
+        assert output == ApiRequestBody.model_validate(expected_output_dict)
+        event = {"body": base64_encode(input_event_str).decode("utf-8")}
+        output = get_parsed_request_body(event)
+        assert output == ApiRequestBody.model_validate(expected_output_dict)
+    @patch.object(wrappers_helpers, "providers")
+    def test_get_url_tile(self, providers_mocked):
+        import xyzservices
+        from samgis.io.wrappers_helpers import get_url_tile
+        from tests import LOCAL_URL_TILE
+        local_tile_provider = xyzservices.TileProvider(name="local_tile_provider", url=LOCAL_URL_TILE, attribution="")
+        expected_output = {'name': 'local_tile_provider', 'url': LOCAL_URL_TILE, 'attribution': ''}
+        providers_mocked.query_name.return_value = local_tile_provider
+        assert get_url_tile("OpenStreetMap") == expected_output
+        local_url = 'http://localhost:8000/{parameter}/{z}/{x}/{y}.png'
+        local_tile_provider = xyzservices.TileProvider(
+            name="local_tile_provider_param", url=local_url, attribution="", parameter="lamda_handler"
+        )
+        providers_mocked.query_name.return_value = local_tile_provider
+        assert get_url_tile("OpenStreetMap.HOT") == {
+            "parameter": "lamda_handler", 'name': 'local_tile_provider_param', 'url': local_url, 'attribution': ''
+        }
+    @staticmethod
+    def test_get_url_tile_real():
+        from samgis.io.wrappers_helpers import get_url_tile
+        assert get_url_tile("OpenStreetMap") == {
+            'url': 'https://tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19,
+            'html_attribution': '&copy; <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',
+            'attribution': '(C) OpenStreetMap contributors',
+            'name': 'OpenStreetMap.Mapnik'}
+        html_attribution_hot = '&copy; <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors, '
+        html_attribution_hot += 'Tiles style by <a href="https://www.hotosm.org/" target="_blank">Humanitarian '
+        html_attribution_hot += 'OpenStreetMap Team</a> hosted by <a href="https://openstreetmap.fr/" target="_blank">'
+        html_attribution_hot += 'OpenStreetMap France</a>'
+        attribution_hot = '(C) OpenStreetMap contributors, Tiles style by Humanitarian OpenStreetMap Team hosted by '
+        attribution_hot += 'OpenStreetMap France'
+        assert get_url_tile("OpenStreetMap.HOT") == {
+            'url': 'https://{s}.tile.openstreetmap.fr/hot/{z}/{x}/{y}.png', 'max_zoom': 19,
+            'html_attribution': html_attribution_hot, 'attribution': attribution_hot, 'name': 'OpenStreetMap.HOT'
+        }

tests/prediction_api/test_sam_onnx.py DELETED Viewed

@@ -1,90 +0,0 @@
-import logging
-import unittest
-import numpy as np
-from samgis import MODEL_FOLDER
-from samgis.prediction_api.sam_onnx import SegmentAnythingONNX
-from samgis.utilities.constants import MODEL_ENCODER_NAME, MODEL_DECODER_NAME
-from samgis.utilities.utilities import hash_calculate
-from tests import TEST_EVENTS_FOLDER
-instance_sam_onnx = SegmentAnythingONNX(
-    encoder_model_path=MODEL_FOLDER / MODEL_ENCODER_NAME,
-    decoder_model_path=MODEL_FOLDER / MODEL_DECODER_NAME
-)
-np_img = np.load(TEST_EVENTS_FOLDER / "samexporter_predict" / "oceania" / "img.npy")
-prompt = [{
-    "type": "point",
-    "data": [934, 510],
-    "label": 0
-}]
-class TestSegmentAnythingONNX(unittest.TestCase):
-    def test_encode_predict_masks_ok(self):
-        embedding = instance_sam_onnx.encode(np_img)
-        try:
-            assert hash_calculate(embedding) == b"m2O3y7pNUwlLuAZhBHkRIu8cDIIej0oOmWOXevs39r4="
-        except AssertionError as ae1:
-            logging.warning(f"ae1:{ae1}.")
-        inference_mask = instance_sam_onnx.predict_masks(embedding, prompt)
-        try:
-            assert hash_calculate(inference_mask) == b'YSKKNCs3AMpbeDUVwqIwNQqJ365OG4239hxjFnW7XTM='
-        except AssertionError as ae2:
-            logging.warning(f"ae2:{ae2}.")
-        mask_output = np.zeros((inference_mask.shape[2], inference_mask.shape[3]), dtype=np.uint8)
-        for n, m in enumerate(inference_mask[0, :, :, :]):
-            logging.debug(f"{n}th of prediction_masks shape {inference_mask.shape}"
-                          f" => mask shape:{mask_output.shape}, {mask_output.dtype}.")
-            mask_output[m > 0.0] = 255
-        mask_expected = np.load(TEST_EVENTS_FOLDER / "SegmentAnythingONNX" / "mask_output.npy")
-        # assert MAP (mean average precision) is 100%
-        # sum expected mask to output mask:
-        # - asserted "good" inference values are 2 (matched object) or 0 (matched background)
-        # - "bad" inference value is 1 (there are differences between expected and output mask)
-        sum_mask_output_vs_expected = mask_expected / 255 + mask_output / 255
-        unique_values__output_vs_expected = np.unique(sum_mask_output_vs_expected, return_counts=True)
-        tot = sum_mask_output_vs_expected.size
-        perc = {
-            k: 100 * v / tot for
-            k, v in
-            zip(unique_values__output_vs_expected[0], unique_values__output_vs_expected[1])
-        }
-        try:
-            assert 1 not in perc
-        except AssertionError:
-            n_pixels = perc[1]
-            logging.error(f"found {n_pixels:.2%} different pixels between expected masks and output mask.")
-            # try to assert that the % of different pixels are minor than 5%
-            assert perc[1] < 5
-    def test_encode_predict_masks_ex1(self):
-        with self.assertRaises(Exception):
-            try:
-                np_input = np.zeros((10, 10))
-                instance_sam_onnx.encode(np_input)
-            except Exception as e:
-                logging.error(f"e:{e}.")
-                msg = "[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: input_image "
-                msg += "Got: 2 Expected: 3 Please fix either the inputs or the model."
-                assert str(e) == msg
-                raise e
-    def test_encode_predict_masks_ex2(self):
-        wrong_prompt = [{
-            "type": "rectangle",
-            "data": [934, 510],
-            "label": 0
-        }]
-        embedding = instance_sam_onnx.encode(np_img)
-        with self.assertRaises(IndexError):
-            try:
-                instance_sam_onnx.predict_masks(embedding, wrong_prompt)
-            except IndexError as ie:
-                print(ie)
-                assert str(ie) == "list index out of range"
-                raise ie

tests/test_fastapi_app.py CHANGED Viewed

@@ -12,6 +12,10 @@ from tests.local_tiles_http_server import LocalTilesHttpServer
 from wrappers import fastapi_wrapper
 from wrappers.fastapi_wrapper import app
 client = TestClient(app)
 source = {
     'url': 'https://tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19,
@@ -95,11 +99,11 @@ class TestFastapiApp(unittest.TestCase):
         assert response.status_code == 404
     def test_infer_samgis_422(self):
-        response = client.post("/infer_samgis", json={})
-        print("response.status_code:", response.status_code)
         assert response.status_code == 422
         body_loaded = response.json()
-        print("response.body_loaded:", body_loaded)
         assert body_loaded == {"msg": "Error - Unprocessable Entity"}
     def test_infer_samgis_middleware_500(self):
@@ -107,11 +111,11 @@ class TestFastapiApp(unittest.TestCase):
         local_event = deepcopy(event)
         local_event["source_type"] = "source_fake"
-        response = client.post("/infer_samgis", json=local_event)
-        print("response.status_code:", response.status_code)
         assert response.status_code == 500
         body_loaded = response.json()
-        print("response.body_loaded:", body_loaded)
         assert body_loaded == {'success': False}
     @patch.object(time, "time")
@@ -120,11 +124,11 @@ class TestFastapiApp(unittest.TestCase):
         time_mocked.return_value = 0
         samexporter_predict_mocked.side_effect = ValueError("I raise a value error!")
-        response = client.post("/infer_samgis", json=event)
-        print("response.status_code:", response.status_code)
         assert response.status_code == 500
         body = response.json()
-        print("response.body:", body)
         assert body == {'msg': 'Error - Internal Server Error'}
     @patch.object(wrappers_helpers, "get_url_tile")
@@ -141,19 +145,19 @@ class TestFastapiApp(unittest.TestCase):
         get_url_tile_mocked.return_value = local_tile_provider
         with LocalTilesHttpServer.http_server("localhost", listen_port, directory=TEST_EVENTS_FOLDER):
-            response = client.post("/infer_samgis", json=event)
-            print("response.status_code:", response.status_code)
         assert response.status_code == 200
         body_string = response.json()["body"]
         body_loaded = json.loads(body_string)
-        print("response.body_loaded:", body_loaded)
         assert "duration_run" in body_loaded
         output = body_loaded["output"]
         assert 'n_predictions' in output
         assert "n_shapes_geojson" in output
         geojson = output["geojson"]
         output_geojson = shapely.from_geojson(geojson)
-        print("output_geojson::", type(output_geojson))
         assert isinstance(output_geojson, shapely.GeometryCollection)
         assert len(output_geojson.geoms) == 3
@@ -173,10 +177,10 @@ class TestFastapiApp(unittest.TestCase):
         }
         samexporter_predict_mocked.return_value = samexporter_output
-        response = client.post("/infer_samgis", json=event)
-        print("response.status_code:", response.status_code)
         assert response.status_code == 200
         response_json = response.json()
         body_loaded = json.loads(response_json["body"])
-        print("response.body_loaded:", body_loaded)
         self.assertDictEqual(body_loaded, {'duration_run': 0, 'output': samexporter_output})

 from wrappers import fastapi_wrapper
 from wrappers.fastapi_wrapper import app
+infer_samgis = "/infer_samgis"
+response_status_code = "response.status_code:{}."
+response_body_loaded = "response.body_loaded:{}."
 client = TestClient(app)
 source = {
     'url': 'https://tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19,
         assert response.status_code == 404
     def test_infer_samgis_422(self):
+        response = client.post(infer_samgis, json={})
+        print(response_status_code.format(response.status_code))
         assert response.status_code == 422
         body_loaded = response.json()
+        print(response_body_loaded.format(body_loaded))
         assert body_loaded == {"msg": "Error - Unprocessable Entity"}
     def test_infer_samgis_middleware_500(self):
         local_event = deepcopy(event)
         local_event["source_type"] = "source_fake"
+        response = client.post(infer_samgis, json=local_event)
+        print(response_status_code.format(response.status_code))
         assert response.status_code == 500
         body_loaded = response.json()
+        print(response_body_loaded.format(body_loaded))
         assert body_loaded == {'success': False}
     @patch.object(time, "time")
         time_mocked.return_value = 0
         samexporter_predict_mocked.side_effect = ValueError("I raise a value error!")
+        response = client.post(infer_samgis, json=event)
+        print(response_status_code.format(response.status_code))
         assert response.status_code == 500
         body = response.json()
+        print(response_body_loaded.format(body))
         assert body == {'msg': 'Error - Internal Server Error'}
     @patch.object(wrappers_helpers, "get_url_tile")
         get_url_tile_mocked.return_value = local_tile_provider
         with LocalTilesHttpServer.http_server("localhost", listen_port, directory=TEST_EVENTS_FOLDER):
+            response = client.post(infer_samgis, json=event)
+            print(response_status_code.format(response.status_code))
         assert response.status_code == 200
         body_string = response.json()["body"]
         body_loaded = json.loads(body_string)
+        print(response_body_loaded.format(body_loaded))
         assert "duration_run" in body_loaded
         output = body_loaded["output"]
         assert 'n_predictions' in output
         assert "n_shapes_geojson" in output
         geojson = output["geojson"]
         output_geojson = shapely.from_geojson(geojson)
+        print("output_geojson::{}.".format(output_geojson))
         assert isinstance(output_geojson, shapely.GeometryCollection)
         assert len(output_geojson.geoms) == 3
         }
         samexporter_predict_mocked.return_value = samexporter_output
+        response = client.post(infer_samgis, json=event)
+        print(response_status_code.format(response.status_code))
         assert response.status_code == 200
         response_json = response.json()
         body_loaded = json.loads(response_json["body"])
+        print(response_body_loaded.format(body_loaded))
         self.assertDictEqual(body_loaded, {'duration_run': 0, 'output': samexporter_output})

tests/utilities/__init__.py DELETED Viewed

File without changes

tests/utilities/test_serialize.py DELETED Viewed

@@ -1,96 +0,0 @@
-import unittest
-import numpy as np
-from samgis.utilities.serialize import serialize
-test_dict_list_dict = {
-    "type": "FeatureCollection",
-    "name": "volcanoes",
-    "crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}},
-    "features": [
-        {"type": "Feature", "properties": {"Volcano_Number": 283010, "Volcano_Name": "Izu-Tobu", "prop_none": None},
-         "geometry": {"type": "Point", "coordinates": [139.098, 34.9]}},
-        {"type": "Feature",
-         "properties": {"Volcano_Number": 283020, "Volcano_Name": "Hakoneyama", "ndarray": np.array([1])},
-         "geometry": {"type": "Point", "coordinates": [139.021, 35.233]}}
-    ]
-}
-class TestSerialize(unittest.TestCase):
-    def test_serialize(self):
-        from bson import ObjectId
-        # remove keys with values as bson.ObjectId
-        d1 = {"_id": ObjectId()}
-        self.assertDictEqual(serialize(d1), dict())
-        # test: serialize nd.float*, number as key => str
-        np_int_4 = np.asarray([87], dtype=np.integer)[0]
-        d2 = {"b": np.float32(45.0), 3: 33, 1.56: np_int_4, 3.5: 44.0, "d": "b", "tuple": (1, 2)}
-        expected_d2 = {
-            'b': 45.0,
-            3: 33,
-            1.56: 87,
-            3.5: 44.0,
-            'd': 'b',
-            "tuple": [1, 2]
-        }
-        serialized_d2 = serialize(d2)
-        self.assertDictEqual(serialized_d2, expected_d2)
-        # # nested dict of list of dict, serialize nd.array
-        d3 = {"e": [{"q": 123}, {"q": 456}], "a": np.arange(1.1, 16.88).reshape(4, 4)}
-        expected_d3 = {
-            "e": [{"q": 123}, {"q": 456}],
-            'a': [[1.1, 2.1, 3.1, 4.1], [5.1, 6.1, 7.1, 8.1], [9.1, 10.1, 11.1, 12.1], [13.1, 14.1, 15.1, 16.1]]
-        }
-        self.assertDictEqual(serialize(d3), expected_d3)
-    def test_serialize_dict_exception(self):
-        from json import JSONDecodeError
-        e = JSONDecodeError(msg="x", doc="what we are?", pos=111)
-        exception = serialize({"k": e})
-        self.assertDictEqual(
-            exception,
-            {'k': {'msg': 'x', 'type': "<class 'json.decoder.JSONDecodeError'>", 'doc': 'what we are?', 'pos': 111,
-                   'lineno': 1, 'colno': 112}}
-        )
-    def test_serialize_bytes(self):
-        self.assertDictEqual(
-            serialize({"k": b"x"}),
-            {'k': {'value': 'eA==', 'type': 'bytes'}}
-        )
-    def test_serialize_dict_list_dict(self):
-        serialized_dict_no_none = serialize(test_dict_list_dict, include_none=False)
-        self.assertDictEqual(serialized_dict_no_none, {
-            'type': 'FeatureCollection',
-            'name': 'volcanoes',
-            'crs': {'type': 'name', 'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}},
-            'features': [
-                {'type': 'Feature', 'properties': {'Volcano_Number': 283010, 'Volcano_Name': 'Izu-Tobu'},
-                 'geometry': {'type': 'Point', 'coordinates': [139.098, 34.9]}},
-                {'type': 'Feature',
-                 'properties': {'Volcano_Number': 283020, 'Volcano_Name': 'Hakoneyama', 'ndarray': [1]},
-                 'geometry': {'type': 'Point', 'coordinates': [139.021, 35.233]}}
-            ]
-        })
-        serialized_dict_wiht_none = serialize(test_dict_list_dict, include_none=True)
-        self.assertDictEqual(serialized_dict_wiht_none, {
-            'type': 'FeatureCollection',
-            'name': 'volcanoes',
-            'crs': {'type': 'name', 'properties': {'name': 'urn:ogc:def:crs:OGC:1.3:CRS84'}},
-            'features': [
-                {'type': 'Feature',
-                 'properties': {'Volcano_Number': 283010, 'Volcano_Name': 'Izu-Tobu', 'prop_none': None},
-                 'geometry': {'type': 'Point', 'coordinates': [139.098, 34.9]}},
-                {'type': 'Feature',
-                 'properties': {'Volcano_Number': 283020, 'Volcano_Name': 'Hakoneyama', 'ndarray': [1]},
-                 'geometry': {'type': 'Point', 'coordinates': [139.021, 35.233]}}
-            ]
-        })

wrappers/fastapi_wrapper.py CHANGED Viewed

@@ -10,7 +10,7 @@ from pydantic import ValidationError
 from samgis import PROJECT_ROOT_FOLDER
 from samgis.io.wrappers_helpers import get_parsed_bbox_points
 from samgis.utilities.type_hints import ApiRequestBody
-from samgis.utilities.fastapi_logger import setup_logging
 from samgis.prediction_api.predictors import samexporter_predict
@@ -33,8 +33,9 @@ async def request_middleware(request, call_next):
         finally:
             response.headers["X-Request-ID"] = request_id
-            app_logger.info(f"Request ended")
-            return response
 @app.post("/post_test")
@@ -49,7 +50,10 @@ async def post_test(request_input: ApiRequestBody) -> JSONResponse:
 @app.get("/health")
 async def health() -> JSONResponse:
-    app_logger.info(f"hello")
     return JSONResponse(status_code=200, content={"msg": "still alive..."})

 from samgis import PROJECT_ROOT_FOLDER
 from samgis.io.wrappers_helpers import get_parsed_bbox_points
 from samgis.utilities.type_hints import ApiRequestBody
+from samgis_core.utilities.fastapi_logger import setup_logging
 from samgis.prediction_api.predictors import samexporter_predict
         finally:
             response.headers["X-Request-ID"] = request_id
+            app_logger.info("Request ended")
+        return response
 @app.post("/post_test")
 @app.get("/health")
 async def health() -> JSONResponse:
+    from samgis.__version__ import __version__ as version
+    from samgis_core.__version__ import __version__ as version_core
+    app_logger.info(f"still alive, version:{version}, version_core:{version_core}.")
     return JSONResponse(status_code=200, content={"msg": "still alive..."})