add black and isort check to ci + reformat codebase (#179)

* add black check to ci workflow

* update dev package versions

* add isort check for ci

* reformat with isort

* reformat with upodate black and isort

* reformat config files with black

* update readme with new contributing guidelines

* update project toml with isort config

.github/workflows/ci.yml

@@ -71,11 +71,13 @@ jobs:
         run: >
           pip install mmcv-full==1.3.7 mmdet==2.13.0 yolov5==5.0.6 norfair==0.3.0
 
-      - name: Lint with flake8
+      - name: Lint with flake8, black and isort
         run: |
-          pip install flake8
+          pip install -e .[dev]
           # stop the build if there are Python syntax errors or undefined names
           flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+          black . --check --config pyproject.toml
+          isort -c .
           # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
           flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
 

README.md

@@ -405,6 +405,22 @@ mot_video.export(export_dir="mot_gt", type="gt")
 
 All you need to do is, creating a new class in [model.py](sahi/model.py) that implements [DetectionModel class](https://github.com/obss/sahi/blob/651f8e6cdb20467815748764bb198dd50241ab2b/sahi/model.py#L10). You can take the [MMDetection wrapper](https://github.com/obss/sahi/blob/651f8e6cdb20467815748764bb198dd50241ab2b/sahi/model.py#L164) or [YOLOv5 wrapper](https://github.com/obss/sahi/blob/ffa168fc38b75a002a0117f1fdde9470e1a9ce8c/sahi/model.py#L363) as a reference.
 
+Before opening a PR:
+
+- Install required development packages:
+
+```bash
+pip install -U -e .[dev]
+```
+
+- Reformat with black and isort:
+
+```bash
+black . --config pyproject.toml
+isort .
+```
+
+
 ## <div align="center">Contributers</div>
 
 <div align="center">

pyproject.toml

@@ -1,2 +1,6 @@
 [tool.black]
-line-length = 120
+line-length = 120
+
+[tool.isort]
+line_length = 120
+profile = "black"

sahi/annotation.py

@@ -2,7 +2,7 @@
 # Code written by Fatih C Akyon, 2020.
 
 import copy
-from typing import List, Optional, Dict
+from typing import Dict, List, Optional
 
 import numpy as np
 
@@ -111,7 +111,11 @@ def __repr__(self):
 class Mask:
     @classmethod
     def from_float_mask(
-        cls, mask, full_shape=None, mask_threshold: float = 0.5, shift_amount: list = [0, 0],
+        cls,
+        mask,
+        full_shape=None,
+        mask_threshold: float = 0.5,
+        shift_amount: list = [0, 0],
     ):
         """
         Args:
@@ -126,11 +130,18 @@ def from_float_mask(
                 Size of the full image after shifting, should be in the form of [height, width]
         """
         bool_mask = mask > mask_threshold
-        return cls(bool_mask=bool_mask, shift_amount=shift_amount, full_shape=full_shape,)
+        return cls(
+            bool_mask=bool_mask,
+            shift_amount=shift_amount,
+            full_shape=full_shape,
+        )
 
     @classmethod
     def from_coco_segmentation(
-        cls, segmentation, full_shape=None, shift_amount: list = [0, 0],
+        cls,
+        segmentation,
+        full_shape=None,
+        shift_amount: list = [0, 0],
     ):
         """
         Init Mask from coco segmentation representation.
@@ -152,10 +163,17 @@ def from_coco_segmentation(
         assert full_shape is not None, "full_shape must be provided"
 
         bool_mask = get_bool_mask_from_coco_segmentation(segmentation, height=full_shape[0], width=full_shape[1])
-        return cls(bool_mask=bool_mask, shift_amount=shift_amount, full_shape=full_shape,)
+        return cls(
+            bool_mask=bool_mask,
+            shift_amount=shift_amount,
+            full_shape=full_shape,
+        )
 
     def __init__(
-        self, bool_mask=None, full_shape=None, shift_amount: list = [0, 0],
+        self,
+        bool_mask=None,
+        full_shape=None,
+        shift_amount: list = [0, 0],
     ):
         """
         Args:
@@ -216,7 +234,14 @@ def get_shifted_mask(self):
         # Confirm full_shape is specified
         assert (self.full_shape_height is not None) and (self.full_shape_width is not None), "full_shape is None"
         # init full mask
-        mask_fullsized = np.full((self.full_shape_height, self.full_shape_width,), 0, dtype="float32",)
+        mask_fullsized = np.full(
+            (
+                self.full_shape_height,
+                self.full_shape_width,
+            ),
+            0,
+            dtype="float32",
+        )
 
         # arrange starting ending indexes
         starting_pixel = [self.shift_x, self.shift_y]
@@ -230,7 +255,11 @@ def get_shifted_mask(self):
             : ending_pixel[1] - starting_pixel[1], : ending_pixel[0] - starting_pixel[0]
         ]
 
-        return Mask(mask_fullsized, shift_amount=[0, 0], full_shape=self.full_shape,)
+        return Mask(
+            mask_fullsized,
+            shift_amount=[0, 0],
+            full_shape=self.full_shape,
+        )
 
     def to_coco_segmentation(self):
         """
@@ -441,7 +470,10 @@ def from_shapely_annotation(
 
     @classmethod
     def from_imantics_annotation(
-        cls, annotation, shift_amount: Optional[List[int]] = [0, 0], full_shape: Optional[List[int]] = None,
+        cls,
+        annotation,
+        shift_amount: Optional[List[int]] = [0, 0],
+        full_shape: Optional[List[int]] = None,
     ):
         """
         Creates ObjectAnnotation from imantics.annotation.Annotation
@@ -495,11 +527,18 @@ def __init__(
             self.mask = None
             self.bbox = BoundingBox(bbox, shift_amount)
         else:
-            self.mask = Mask(bool_mask=bool_mask, shift_amount=shift_amount, full_shape=full_shape,)
+            self.mask = Mask(
+                bool_mask=bool_mask,
+                shift_amount=shift_amount,
+                full_shape=full_shape,
+            )
             bbox = get_bbox_from_bool_mask(bool_mask)
             self.bbox = BoundingBox(bbox, shift_amount)
         category_name = category_name if category_name else str(category_id)
-        self.category = Category(id=category_id, name=category_name,)
+        self.category = Category(
+            id=category_id,
+            name=category_name,
+        )
 
         self.merged = None
 
@@ -515,7 +554,9 @@ def to_coco_annotation(self):
             )
         else:
             coco_annotation = CocoAnnotation.from_coco_bbox(
-                bbox=self.bbox.to_coco_bbox(), category_id=self.category.id, category_name=self.category.name,
+                bbox=self.bbox.to_coco_bbox(),
+                category_id=self.category.id,
+                category_name=self.category.name,
             )
         return coco_annotation
 
@@ -532,7 +573,10 @@ def to_coco_prediction(self):
             )
         else:
             coco_prediction = CocoPrediction.from_coco_bbox(
-                bbox=self.bbox.to_coco_bbox(), category_id=self.category.id, category_name=self.category.name, score=1,
+                bbox=self.bbox.to_coco_bbox(),
+                category_id=self.category.id,
+                category_name=self.category.name,
+                score=1,
             )
         return coco_prediction
 
@@ -545,7 +589,9 @@ def to_shapely_annotation(self):
                 segmentation=self.mask.to_coco_segmentation(),
             )
         else:
-            shapely_annotation = ShapelyAnnotation.from_coco_bbox(bbox=self.bbox.to_coco_bbox(),)
+            shapely_annotation = ShapelyAnnotation.from_coco_bbox(
+                bbox=self.bbox.to_coco_bbox(),
+            )
         return shapely_annotation
 
     def to_imantics_annotation(self):

sahi/model.py

@@ -1,11 +1,12 @@
 # OBSS SAHI Tool
 # Code written by Fatih C Akyon, 2020.
 
+from typing import Dict, List, Optional, Union
+
 import numpy as np
 
 from sahi.prediction import ObjectPrediction
 from sahi.utils.torch import cuda_is_available, empty_cuda_cache
-from typing import List, Dict, Optional, Union
 
 
 class DetectionModel:
@@ -89,7 +90,9 @@ def perform_inference(self, image: np.ndarray, image_size: int = None):
         NotImplementedError()
 
     def _create_object_prediction_list_from_original_predictions(
-        self, shift_amount: Optional[List[int]] = [0, 0], full_shape: Optional[List[int]] = None,
+        self,
+        shift_amount: Optional[List[int]] = [0, 0],
+        full_shape: Optional[List[int]] = None,
     ):
         """
         This function should be implemented in a way that self._original_predictions should
@@ -117,7 +120,9 @@ def _apply_category_remapping(self):
             object_prediction.category.id = new_category_id_int
 
     def convert_original_predictions(
-        self, shift_amount: Optional[List[int]] = [0, 0], full_shape: Optional[List[int]] = None,
+        self,
+        shift_amount: Optional[List[int]] = [0, 0],
+        full_shape: Optional[List[int]] = None,
     ):
         """
         Converts original predictions of the detection model to a list of
@@ -130,7 +135,8 @@ def convert_original_predictions(
                 Size of the full image after shifting, should be in the form of [height, width]
         """
         self._create_object_prediction_list_from_original_predictions(
-            shift_amount=shift_amount, full_shape=full_shape,
+            shift_amount=shift_amount,
+            full_shape=full_shape,
         )
         if self.category_remapping:
             self._apply_category_remapping()
@@ -143,7 +149,9 @@ def object_prediction_list(self):
     def original_predictions(self):
         return self._original_predictions
 
-    def _create_predictions_from_object_prediction_list(object_prediction_list: List[ObjectPrediction],):
+    def _create_predictions_from_object_prediction_list(
+        object_prediction_list: List[ObjectPrediction],
+    ):
         """
         This function should be implemented in a way that it converts a list of
         prediction.ObjectPrediction instance to detection model's original prediction format.
@@ -172,7 +180,11 @@ def load_model(self):
         from mmdet.apis import init_detector
 
         # set model
-        model = init_detector(config=self.config_path, checkpoint=self.model_path, device=self.device,)
+        model = init_detector(
+            config=self.config_path,
+            checkpoint=self.model_path,
+            device=self.device,
+        )
         self.model = model
 
         # set category_mapping
@@ -239,7 +251,9 @@ def category_names(self):
             return self.model.CLASSES
 
     def _create_object_prediction_list_from_original_predictions(
-        self, shift_amount: Optional[List[int]] = [0, 0], full_shape: Optional[List[int]] = None,
+        self,
+        shift_amount: Optional[List[int]] = [0, 0],
+        full_shape: Optional[List[int]] = None,
     ):
         """
         self._original_predictions is converted to a list of prediction.ObjectPrediction and set to
@@ -294,7 +308,8 @@ def _create_object_prediction_list_from_original_predictions(
         self._object_prediction_list = object_prediction_list
 
     def _create_original_predictions_from_object_prediction_list(
-        self, object_prediction_list: List[ObjectPrediction],
+        self,
+        object_prediction_list: List[ObjectPrediction],
     ):
         """
         Converts a list of prediction.ObjectPrediction instance to detection model's original prediction format.
@@ -412,7 +427,9 @@ def category_names(self):
         return self.model.names
 
     def _create_object_prediction_list_from_original_predictions(
-        self, shift_amount: Optional[List[int]] = [0, 0], full_shape: Optional[List[int]] = None,
+        self,
+        shift_amount: Optional[List[int]] = [0, 0],
+        full_shape: Optional[List[int]] = None,
     ):
         """
         self._original_predictions is converted to a list of prediction.ObjectPrediction and set to
@@ -456,7 +473,8 @@ def _create_object_prediction_list_from_original_predictions(
         self._object_prediction_list = object_prediction_list
 
     def _create_original_predictions_from_object_prediction_list(
-        self, object_prediction_list: List[ObjectPrediction],
+        self,
+        object_prediction_list: List[ObjectPrediction],
     ):
         """
         Converts a list of prediction.ObjectPrediction instance to detection model's original