Make style

Sorry for all the updates, forgot to do "make style"

Author: Tyler Odenthal

roboflow/core/project.py

@@ -532,10 +532,6 @@ def single_upload(
 
     def __str__(self):
         # String representation of project
-        json_str = {
-            "name": self.name,
-            "type": self.type,
-            "workspace": self.__workspace,
-        }
+        json_str = {"name": self.name, "type": self.type, "workspace": self.__workspace}
 
         return json.dumps(json_str, indent=2)

roboflow/core/version.py

@@ -94,15 +94,9 @@ def __init__(
                     local=local,
                 )
             elif self.type == TYPE_INSTANCE_SEGMENTATION:
-                self.model = InstanceSegmentationModel(
-                    self.__api_key,
-                    self.id,
-                )
+                self.model = InstanceSegmentationModel(self.__api_key, self.id)
             elif self.type == TYPE_SEMANTIC_SEGMENTATION:
-                self.model = SemanticSegmentationModel(
-                    self.__api_key,
-                    self.id,
-                )
+                self.model = SemanticSegmentationModel(self.__api_key, self.id)
             else:
                 self.model = None
 
@@ -401,10 +395,7 @@ def __get_format_identifier(self, format):
                 "You must pass a format argument to version.download() or define a model in your Roboflow object"
             )
 
-        friendly_formats = {
-            "yolov5": "yolov5pytorch",
-            "yolov7": "yolov7pytorch",
-        }
+        friendly_formats = {"yolov5": "yolov5pytorch", "yolov7": "yolov7pytorch"}
         return friendly_formats.get(format, format)
 
     def __reformat_yaml(self, location, format):

roboflow/models/inference.py

@@ -31,10 +31,7 @@ def __get_image_params(self, image_path):
         """
         validate_image_path(image_path)
 
-        hosted_image = urllib.parse.urlparse(image_path).scheme in (
-            "http",
-            "https",
-        )
+        hosted_image = urllib.parse.urlparse(image_path).scheme in ("http", "https")
 
         if hosted_image:
             return {"image": image_path}, {}
@@ -45,10 +42,7 @@ def __get_image_params(self, image_path):
         data = MultipartEncoder(
             fields={"file": ("imageToUpload", buffered.getvalue(), "image/jpeg")}
         )
-        return {}, {
-            "data": data,
-            "headers": {"Content-Type": data.content_type},
-        }
+        return {}, {"data": data, "headers": {"Content-Type": data.content_type}}
 
     def predict(self, image_path, prediction_type=None, **kwargs):
         """
@@ -71,7 +65,5 @@ def predict(self, image_path, prediction_type=None, **kwargs):
         response.raise_for_status()
 
         return PredictionGroup.create_prediction_group(
-            response.json(),
-            image_path=image_path,
-            prediction_type=prediction_type,
+            response.json(), image_path=image_path, prediction_type=prediction_type
         )

roboflow/util/image_utils.py

@@ -22,10 +22,7 @@ def check_image_url(url):
     :param url: URL of image
     :returns: Boolean
     """
-    if urllib.parse.urlparse(url).scheme not in (
-        "http",
-        "https",
-    ):
+    if urllib.parse.urlparse(url).scheme not in ("http", "https"):
         return False
 
     r = requests.head(url)

roboflow/util/prediction.py

@@ -80,10 +80,7 @@ def plot_annotation(axes, prediction=None, stroke=1, transparency=60):
     elif prediction["prediction_type"] == INSTANCE_SEGMENTATION_MODEL:
         points = [[p["x"], p["y"]] for p in prediction["points"]]
         polygon = patches.Polygon(
-            points,
-            linewidth=stroke,
-            edgecolor="r",
-            facecolor="none",
+            points, linewidth=stroke, edgecolor="r", facecolor="none"
         )
         axes.add_patch(polygon)
     elif prediction["prediction_type"] == SEMANTIC_SEGMENTATION_MODEL:
@@ -500,19 +497,28 @@ def create_prediction_group(json_response, image_path, prediction_type):
                 )
                 prediction_list.append(prediction)
                 if "image" not in json_response:
-                    json_response["image"] = {'width': dimensions[0], 'height': dimensions[1]}
+                    json_response["image"] = {
+                        "width": dimensions[0],
+                        "height": dimensions[1],
+                    }
             img_dims = json_response["image"]
         elif prediction_type == CLASSIFICATION_MODEL:
             prediction = Prediction(json_response, image_path, prediction_type)
             prediction_list.append(prediction)
             if "image" not in json_response:
-                    json_response["image"] = {'width': dimensions[0], 'height': dimensions[1]}
+                json_response["image"] = {
+                    "width": dimensions[0],
+                    "height": dimensions[1],
+                }
             img_dims = json_response["image"]
         elif prediction_type == SEMANTIC_SEGMENTATION_MODEL:
             prediction = Prediction(json_response, image_path, prediction_type)
             prediction_list.append(prediction)
             if "image" not in json_response:
-                    json_response["image"] = {'width': dimensions[0], 'height': dimensions[1]}
+                json_response["image"] = {
+                    "width": dimensions[0],
+                    "height": dimensions[1],
+                }
             img_dims = json_response["image"]
 
         # Seperate list and return as a prediction group