add CI

.github/workflows/test.yml

@@ -0,0 +1,40 @@
+name: SDK Test
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  sdk-test:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4
+
+      - name: Setup Python
+        uses: actions/setup-python@39cd14951b08e74b54015e9e001cdefcf80e669f # v5
+        with:
+          cache: pip
+          python-version: "3.10.11"
+
+      - name: Install dependencies
+        run: |
+          pip install -r requirements.txt
+
+      - name: Install Python tools
+        run: |
+          pip install black==22.10.0 flake8==5.0.4 isort==5.11.5
+
+      - name: Run black
+        run: black --check .
+
+      - name: Run flake8
+        run: flake8 .
+
+      - name: Run isort
+        run: isort --check .

fastlabel/__init__.py

@@ -70,14 +70,14 @@ def find_image_classification_task(self, task_id: str) -> dict:
         """
         endpoint = "tasks/image/classification/" + task_id
         return self.api.get_request(endpoint)
-    
+
     def find_multi_image_classification_task(self, task_id: str) -> dict:
         """
         Find a single multi image classification task.
         """
         endpoint = "tasks/multi-image/classification/" + task_id
         return self.api.get_request(endpoint)
-    
+
     def find_multi_image_classification_task_by_name(
         self, project: str, task_name: str
     ) -> dict:
@@ -432,9 +432,9 @@ def get_image_classification_tasks(
         if limit:
             params["limit"] = limit
         return self.api.get_request(endpoint, params=params)
-    
+
     def get_multi_image_classification_tasks(
-            self,
+        self,
         project: str,
         status: str = None,
         external_status: str = None,
@@ -1197,7 +1197,7 @@ def create_integrated_image_classification_task(
         self.__fill_assign_users(payload, **kwargs)
 
         return self.api.post_request(endpoint, payload=payload)
-    
+
     def create_multi_image_classification_task(
         self,
         project: str,
@@ -2135,7 +2135,7 @@ def update_image_classification_task(
         self.__fill_assign_users(payload, **kwargs)
 
         return self.api.put_request(endpoint, payload=payload)
-    
+
     def update_multi_image_classification_task(
         self,
         task_id: str,
@@ -4300,7 +4300,7 @@ def download_dataset_objects(
                         "annotations": obj["annotations"],
                         "customMetadata": obj["customMetadata"],
                         "tags": obj["tags"],
-                        "object_type": obj["type"]
+                        "object_type": obj["type"],
                     }
                     for obj in objects
                 ]

fastlabel/converters.py

@@ -8,7 +8,7 @@
 from operator import itemgetter
 from pathlib import Path
 from tempfile import NamedTemporaryFile
-from typing import List, Dict, Optional
+from typing import Dict, List, Optional
 
 import cv2
 import geojson
@@ -239,7 +239,7 @@ def __to_coco_annotation(data: dict) -> dict:
         image_id,
         annotation_type,
         annotation_attributes,
-        rotation
+        rotation,
     )
 
 
@@ -254,7 +254,11 @@ def __get_coco_category_by_name(categories: list, name: str) -> Optional[dict]:
 
 def __get_coco_annotation_keypoints(keypoints: list, category_keypoints: list) -> list:
     coco_annotation_keypoints = []
-    keypoint_values = {keypoint["key"]: keypoint["value"] for keypoint in keypoints if keypoint["value"]}
+    keypoint_values = {
+        keypoint["key"]: keypoint["value"]
+        for keypoint in keypoints
+        if keypoint["value"]
+    }
     for category_key in category_keypoints:
         value = keypoint_values.get(category_key, [0, 0, 0])
         # Adjust fastlabel data definition to coco format
@@ -271,20 +275,22 @@ def __get_coco_annotation(
     image_id: str,
     annotation_type: str,
     annotation_attributes: Dict[str, AttributeValue],
-    rotation: int
+    rotation: int,
 ) -> dict:
     annotation = {}
     annotation["num_keypoints"] = len(keypoints) if keypoints else 0
     annotation["keypoints"] = (
-        __get_coco_annotation_keypoints(keypoints, category["keypoints"]) if keypoints else []
+        __get_coco_annotation_keypoints(keypoints, category["keypoints"])
+        if keypoints
+        else []
     )
     annotation["segmentation"] = __to_coco_segmentation(annotation_type, points)
     annotation["iscrowd"] = 0
     annotation["area"] = __to_area(annotation_type, points)
     annotation["image_id"] = image_id
     annotation["bbox"] = (
-        __get_coco_bbox(points, rotation) 
-        if annotation_type == AnnotationType.bbox 
+        __get_coco_bbox(points, rotation)
+        if annotation_type == AnnotationType.bbox
         else __to_bbox(annotation_type, points)
     )
     annotation["rotation"] = rotation
@@ -329,6 +335,7 @@ def __get_rotated_rectangle_coordinates(
 
     return rotated_corners
 
+
 def __get_coco_bbox(
     points: list,
     rotation: int,
@@ -1209,4 +1216,3 @@ def _get_coco_annotation_attributes(annotation: dict) -> Dict[str, AttributeValu
     for attribute in attributes:
         coco_attributes[attribute["key"]] = attribute["value"]
     return coco_attributes
-