Upgraded fairpredictor and API

.github/workflows/docker-image-build-publish.yml

@@ -0,0 +1,48 @@
+#
+name: Create and publish a Docker image
+
+# Configures this workflow to run every time a change is pushed to the branch called `release`.
+on:
+  push:
+    branches:
+      - master
+# Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds.
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu.
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    # Sets the permissions granted to the `GITHUB_TOKEN` for the actions in this job.
+    permissions:
+      contents: read
+      packages: write
+      #
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      # Uses the `docker/login-action` action to log in to the Container registry registry using the account and password that will publish the packages. Once published, the packages are scoped to the account defined here.
+      - name: Log in to the Container registry
+        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      # This step uses [docker/metadata-action](https://github.com/docker/metadata-action#about) to extract tags and labels that will be applied to the specified image. The `id` "meta" allows the output of this step to be referenced in a subsequent step. The `images` value provides the base name for the tags and labels.
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+      # This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages.
+      # It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see "[Usage](https://github.com/docker/build-push-action#usage)" in the README of the `docker/build-push-action` repository.
+      # It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step.
+      - name: Build and push Docker image
+        uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4
+        with:
+          context: .
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

API/main.py

@@ -1,5 +1,6 @@
 import os
 import tempfile
+from typing import List, Optional
 
 import requests
 from fastapi import FastAPI
@@ -14,87 +15,119 @@
 
 
 class PredictionRequest(BaseModel):
-    bbox: list[float] = Field(
-        ...,
-        example=[
-            100.56228021333352,
-            13.685230854641182,
-            100.56383321235313,
-            13.685961853747969,
-        ],
-        description="Bounding box coordinates [min_longitude, min_latitude, max_longitude, max_latitude].",
-    )
+    """
+    Request model for the prediction endpoint.
+    """
+
+    bbox: List[float]
+
     checkpoint: str = Field(
         ...,
         example="path/to/model.tflite or https://example.com/model.tflite",
         description="Path or URL to the machine learning model file.",
     )
-    zoom: int = Field(
+
+    zoom_level: int = Field(
         ...,
-        ge=18,
-        le=23,
-        example=20,
-        description="Zoom level for predictions (between 18 and 23).",
+        description="Zoom level of the tiles to be used for prediction.",
     )
-    tms: str = Field(
+
+    source: str = Field(
         ...,
-        example="https://tiles.openaerialmap.org/6501a65c0906de000167e64d/0/6501a65c0906de000167e64e/{z}/{x}/{y}",
-        description="URL for tile map service.",
+        description="Your Image URL on which you want to detect features.",
     )
-    confidence: float = Field(
-        0.5,
-        example=0.5,
-        gt=0,
-        le=1,
-        description="Threshold probability for filtering out low-confidence predictions. Defaults to 0.5.",
+
+    use_josm_q: Optional[bool] = Field(
+        False,
+        description="Indicates whether to use JOSM query. Defaults to False.",
     )
-    area_threshold: PositiveFloat = Field(
-        3,
-        example=3,
-        description="Threshold for filtering polygon areas. Defaults to 3 sqm.",
+
+    merge_adjacent_polygons: Optional[bool] = Field(
+        True,
+        description="Merges adjacent self-intersecting or containing each other polygons. Defaults to True.",
+    )
+
+    confidence: Optional[int] = Field(
+        50,
+        description="Threshold probability for filtering out low-confidence predictions. Defaults to 50.",
+    )
+
+    max_angle_change: Optional[int] = Field(
+        15,
+        description="Maximum angle change parameter for prediction. Defaults to 15.",
+    )
+
+    skew_tolerance: Optional[int] = Field(
+        15,
+        description="Skew tolerance parameter for prediction. Defaults to 15.",
     )
-    tolerance: PositiveFloat = Field(
+
+    tolerance: Optional[float] = Field(
         0.5,
-        example=0.5,
-        description="Tolerance parameter for simplifying polygons. Defaults to 0.5 m.",
+        description="Tolerance parameter for simplifying polygons. Defaults to 0.5.",
     )
-    tile_overlap_distance: PositiveFloat = Field(
+
+    area_threshold: Optional[float] = Field(
+        3,
+        description="Threshold for filtering polygon areas. Defaults to 3.",
+    )
+
+    tile_overlap_distance: Optional[float] = Field(
         0.15,
-        example=0.15,
-        description="Tile overlap distance to remove the strip between predictions. Defaults to 0.15 m.",
+        description="Provides tile overlap distance to remove the strip between predictions. Defaults to 0.15.",
     )
-    merge_adjacent_polygons: bool = Field(
-        True,
-        example=True,
-        description="Flag to merge adjacent polygons. Defaults to True.",
+
+    @validator(
+        "max_angle_change",
+        "skew_tolerance",
+        "tolerance",
+        "tile_overlap_distance",
+        "area_threshold",
     )
+    def validate_values(self, value):
+        if value is not None:
+            if value < 0 or value > 45:
+                raise ValueError(f"Value should be between 0 and 45: {value}")
+        return value
+
+    @validator("tolerance")
+    def validate_tolerance(self, value):
+        if value is not None:
+            if value < 0 or value > 10:
+                raise ValueError(f"Value should be between 0 and 10: {value}")
+        return value
+
+    @validator("tile_overlap_distance")
+    def validate_tile_overlap_distance(self, value):
+        if value is not None:
+            if value < 0 or value > 1:
+                raise ValueError(f"Value should be between 0 and 1: {value}")
+        return value
+
+    @validator("area_threshold")
+    def validate_area_threshold(self, value):
+        if value is not None:
+            if value < 0 or value > 20:
+                raise ValueError(f"Value should be between 0 and 20: {value}")
+        return value
+
+    @validator("confidence")
+    def validate_confidence(self, value):
+        if value is not None:
+            if value < 0 or value > 100:
+                raise ValueError(f"Value should be between 0 and 100: {value}")
+        return value / 100
 
     @validator("bbox")
-    def validate_bbox_length(cls, value):
-        """
-        Validates the length of bbox coordinates.
-        """
+    def validate_bbox(self, value):
         if len(value) != 4:
-            raise ValueError("bbox must contain 4 float values")
+            raise ValueError("bbox should have exactly 4 elements")
         return value
 
-    @validator("checkpoint")
-    def validate_checkpoint(cls, value):
-        """
-        Validates checkpoint parameter. If URL, download the file to temp directory.
-        """
-        if value.startswith("http"):
-            response = requests.get(value)
-            if response.status_code != 200:
-                raise ValueError(
-                    "Failed to download model checkpoint from the provided URL"
-                )
-            _, temp_file_path = tempfile.mkstemp(suffix=".tflite")
-            with open(temp_file_path, "wb") as f:
-                f.write(response.content)
-            return temp_file_path
-        elif not os.path.exists(value):
-            raise ValueError("Model checkpoint file not found")
+    @validator("zoom_level")
+    def validate_zoom_level(self, value):
+        if value < 18 or value > 22:
+            raise ValueError("Zoom level should be between 18 and 22")
         return value
 
 
@@ -111,15 +144,18 @@ async def predict_api(request: PredictionRequest):
     """
     try:
         predictions = predict(
-            request.bbox,
-            request.checkpoint,
-            request.zoom,
-            request.tms,
+            bbox=request.bbox,
+            model_path=request.checkpoint,
+            zoom_level=request.zoom_level,
+            tms_url=request.source,
+            tile_size=256,
             confidence=request.confidence,
-            area_threshold=request.area_threshold,
-            tolerance=request.tolerance,
             tile_overlap_distance=request.tile_overlap_distance,
             merge_adjancent_polygons=request.merge_adjacent_polygons,
+            max_angle_change=request.max_angle_change,
+            skew_tolerance=request.skew_tolerance,
+            tolerance=request.tolerance,
+            area_threshold=request.area_threshold,
         )
         return predictions
     except Exception as e:

API/requirements.txt

@@ -1,4 +1,4 @@
 fastapi==0.103.2
 uvicorn==0.22.0
-fairpredictor==0.0.29
+fairpredictor
 tflite-runtime==2.14.0

predictor/app.py

@@ -5,6 +5,8 @@
 import time
 import uuid
 
+from orthogonalizer import othogonalize_poly
+
 from .downloader import download
 from .prediction import run_prediction
 from .raster2polygon import polygonizer
@@ -25,6 +27,9 @@ def predict(
     merge_adjancent_polygons=True,
     use_raster2polygon=False,
     remove_metadata=True,
+    use_josm_q=False,
+    max_angle_change=15,
+    skew_tolerance=15,
 ):
     """
     Parameters:
@@ -95,4 +100,13 @@ def predict(
         prediction_geojson_data = json.load(f)
     if remove_metadata:
         shutil.rmtree(base_path)
+    for feature in prediction_geojson_data["features"]:
+        feature["properties"]["building"] = "yes"
+        feature["properties"]["source"] = "fAIr"
+        if use_josm_q is True:
+            feature["geometry"] = othogonalize_poly(
+                feature["geometry"],
+                maxAngleChange=max_angle_change,
+                skewTolerance=skew_tolerance,
+            )
     return prediction_geojson_data

predictor/utils.py

@@ -94,6 +94,21 @@ def latlng2tile(zoom, lat, lng, tile_size):
     return t_x, t_y
 
 
+def tile_xy_to_quad_key(tile_x, tile_y, level_of_detail):
+    quad_key = []
+    for i in range(level_of_detail, 0, -1):
+        digit = "0"
+        mask = 1 << (i - 1)
+        if (tile_x & mask) != 0:
+            digit = chr(ord(digit) + 1)
+        if (tile_y & mask) != 0:
+            digit = chr(ord(digit) + 1)
+            digit = chr(ord(digit) + 1)
+        quad_key.append(digit)
+
+    return "".join(quad_key)
+
+
 def download_imagery(start: list, end: list, zm_level, base_path, source="maxar"):
     """Downloads imagery from start to end tile coordinate system
 
@@ -126,7 +141,9 @@ def download_imagery(start: list, end: list, zm_level, base_path, source="maxar"
                     raise ex
                 source_name = source
                 download_url = f"https://services.digitalglobe.com/earthservice/tmsaccess/tms/1.0.0/DigitalGlobe:ImageryTileService@EPSG:3857@jpg/{zm_level}/{download_path[0]}/{download_path[1]}.jpg?connectId={connect_id}&flipy=true"
-
+            elif source == "bing":
+                download_url = f"https://ecn.t2.tiles.virtualearth.net/tiles/a{tile_xy_to_quad_key(download_path[0],download_path[1],zm_level)}.jpeg?g=14037&pr=odbl&n=z"
+                print(download_url)
             else:
                 # source should be url as string , like this :  https://tiles.openaerialmap.org/62dbd947d8499800053796ec/0/62dbd947d8499800053796ed/{z}/{x}/{y}
                 if "{-y}" in source:

requirements.txt

@@ -4,4 +4,5 @@ tqdm<=4.62.3
 Pillow<=9.0.1
 geopandas<=0.10.2
 shapely
-rasterio
+rasterio
+orthogonalizer

setup.py

@@ -7,7 +7,7 @@
 
 setup(
     name="fairpredictor",
-    version="0.0.29",
+    version="0.0.30",
     url="https://github.com/kshitijrajsharma/fairpredictor",
     author="Kshitij Raj Sharma",
     author_email="[email protected]",
@@ -33,6 +33,6 @@
         "geopandas<=0.14.5",
         "shapely>=1.0.0,<=2.0.2",
         "rasterio>=1.0.0,<=1.3.8",
-        # "raster2polygon",
+        "orthogonalizer",
     ],
 )

tests/test_predict.py

@@ -0,0 +1,12 @@
+bbox = [-84.1334429383278, 9.953153171808898, -84.13033694028854, 9.954719779271468]
+zoom_level = 19
+from predictor import download
+
+image_download_path = download(
+    bbox,
+    zoom_level=zoom_level,
+    tms_url="bing",
+    tile_size=256,
+    download_path="/Users/kshitij/hotosm/fairpredictor/download/test",
+)
+print(image_download_path)