ROI tools

setup.py

@@ -17,7 +17,7 @@
 
 setuptools.setup(
     name="Simba-UW-tf-dev",
-    version="1.91.3",
+    version="1.91.5",
     author="Simon Nilsson, Jia Jie Choong, Sophia Hwang",
     author_email="[email protected]",
     description="Toolkit for computer classification of behaviors in experimental animals",

simba/SimBA.py

@@ -1953,9 +1953,7 @@ def __init__(self):
             wrap="none",
             borderwidth=0,
         )
-        self.txt.insert(
-            INSERT, Defaults.WELCOME_MSG.value + emojis["relaxed"] + "\n" * 2
-        )
+        self.txt.insert(INSERT, Defaults.WELCOME_MSG.value + emojis["relaxed"] + "\n" * 2)
         self.txt.tag_add(TagNames.GREETING.value, "1.0", "3.25")
         y_sb.pack(side=RIGHT, fill=Y)
         self.txt.pack(expand=True, fill="both")

simba/mixins/geometry_mixin.py

@@ -3163,8 +3163,7 @@ def bucket_img_into_grid_hexagon(
         return polygons, round((v_hex_cnt[0] / h_hex_cnt[0]), 3)
 
     @staticmethod
-    def _cumsum_coord_geometries_helper(
-        data: np.ndarray, geometries: Dict[Tuple[int, int], Polygon], verbose: bool
+    def _cumsum_coord_geometries_helper(data: np.ndarray, geometries: Dict[Tuple[int, int], Polygon], verbose: bool
     ):
         data_point = Point(data[1:])
         if verbose:
@@ -3174,14 +3173,13 @@ def _cumsum_coord_geometries_helper(
                 return (int(data[0]), k[0], k[1])
         return (int(data[0]), -1, -1)
 
-    def cumsum_coord_geometries(
-        self,
-        data: np.ndarray,
-        geometries: Dict[Tuple[int, int], Polygon],
-        fps: Optional[int] = None,
-        core_cnt: Optional[int] = -1,
-        verbose: Optional[bool] = True,
-    ):
+    def cumsum_coord_geometries(self,
+                                data: np.ndarray,
+                                geometries: Dict[Tuple[int, int], Polygon],
+                                fps: Optional[int] = None,
+                                core_cnt: Optional[int] = -1,
+                                verbose: Optional[bool] = True):
+
         """
         Compute the cumulative time a body-part has spent inside a grid of geometries using multiprocessing.
 
@@ -3220,7 +3218,7 @@ def cumsum_coord_geometries(
         data = np.hstack((frm_id, data))
         img_arr = np.zeros((data.shape[0], h + 1, w + 1))
         with multiprocessing.Pool(
-            core_cnt, maxtasksperchild=Defaults.LARGE_MAX_TASK_PER_CHILD.value
+            core_cnt, maxtasksperchild=Defaults.MAXIMUM_MAX_TASK_PER_CHILD.value
         ) as pool:
             constants = functools.partial(
                 self._cumsum_coord_geometries_helper,

simba/mixins/image_mixin.py

@@ -51,12 +51,12 @@ def __init__(self):
         pass
 
     @staticmethod
-    def brightness_intensity(
-        imgs: List[np.ndarray], ignore_black: Optional[bool] = True
-    ) -> List[float]:
+    def brightness_intensity(imgs: List[np.ndarray], ignore_black: Optional[bool] = True) -> List[float]:
         """
         Compute the average brightness intensity within each image within a list.
 
+        For example, (i) create a list of images containing a light cue ROI, (ii) compute brightness in each image, (iii) perform kmeans on brightness, and get the frames when the light cue is on vs off.
+
         :param List[np.ndarray] imgs: List of images as arrays to calculate average brightness intensity within.
         :param Optional[bool] ignore_black: If True, ignores black pixels. If the images are sliced non-rectangular geometric shapes created by ``slice_shapes_in_img``, then pixels that don't belong to the shape has been masked in black.
         :returns List[float]: List of floats of size len(imgs) with brightness intensities.
@@ -90,17 +90,8 @@ def brightness_intensity(
     @staticmethod
     def gaussian_blur(img: np.ndarray, kernel_size: Optional[Tuple] = (9, 9)):
         check_if_valid_img(data=img, source=ImageMixin.gaussian_blur.__name__)
-        check_instance(
-            source=ImageMixin.gaussian_blur.__name__,
-            instance=kernel_size,
-            accepted_types=(tuple,),
-        )
-        check_valid_lst(
-            data=list(kernel_size),
-            source=ImageMixin.gaussian_blur.__name__,
-            valid_dtypes=(int,),
-            exact_len=2,
-        )
+        check_instance(source=ImageMixin.gaussian_blur.__name__, instance=kernel_size, accepted_types=(tuple,))
+        check_valid_lst(data=list(kernel_size), source=ImageMixin.gaussian_blur.__name__, valid_dtypes=(int,), exact_len=2,)
         return cv2.GaussianBlur(img, kernel_size, 0)
 
     @staticmethod
@@ -171,13 +162,12 @@ def get_histocomparison(
             )
 
     @staticmethod
-    def get_contourmatch(
-        img_1: np.ndarray,
-        img_2: np.ndarray,
-        mode: Optional[Literal["all", "exterior"]] = "all",
-        method: Optional[Literal["simple", "none", "l2", "kcos"]] = "simple",
-        canny: Optional[bool] = True,
-    ) -> float:
+    def get_contourmatch(img_1: np.ndarray,
+                         img_2: np.ndarray,
+                         mode: Optional[Literal["all", "exterior"]] = "all",
+                         method: Optional[Literal["simple", "none", "l2", "kcos"]] = "simple",
+                         canny: Optional[bool] = True) -> float:
+
         """
         Calculate contour similarity between two images.
 
@@ -396,13 +386,13 @@ def find_contours(
                     interior_contours.append(cnts[i])
 
     @staticmethod
-    def orb_matching_similarity_(
-        img_1: np.ndarray,
-        img_2: np.ndarray,
-        method: Literal["knn", "match", "radius"] = "knn",
-        mask: Optional[np.ndarray] = None,
-        threshold: Optional[int] = 0.75,
-    ) -> int:
+    def orb_matching_similarity_(img_1: np.ndarray,
+                                 img_2: np.ndarray,
+                                 method: Literal["knn", "match", "radius"] = "knn",
+                                 mask: Optional[np.ndarray] = None,
+                                 threshold: Optional[int] = 0.75) -> int:
+
+
         """Perform ORB feature matching between two sets of images.
 
         >>> img_1 = cv2.imread('/Users/simon/Desktop/envs/troubleshooting/khan/project_folder/videos/stitched_frames/0.png').astype(np.uint8)
@@ -416,9 +406,7 @@ def orb_matching_similarity_(
         sliced_matches = None
         if method == "knn":
             matches = cv2.BFMatcher().knnMatch(des1, des2, k=2)
-            sliced_matches = [
-                m for m, n in matches if m.distance < threshold * n.distance
-            ]
+            sliced_matches = [m for m, n in matches if m.distance < threshold * n.distance]
         if method == "match":
             matches = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True).match(des1, des2)
             sliced_matches = [match for match in matches if match.distance <= threshold]