fix: update function type hints from np.array to np.ndarray for consi…

…stency

livecellx/core/datasets.py

@@ -376,7 +376,7 @@ def get_img_by_idx(self, idx):
         img = self.read_img_url_func(url)
         return img
 
-    def get_img_by_time(self, time) -> np.array:
+    def get_img_by_time(self, time) -> np.ndarray:
         """Get an image by time"""
         return self.read_img_url_func(self.time2url[time])
 
@@ -473,7 +473,7 @@ def __init__(self, img, name=None, ext=".png", in_memory=True):
     def read_single_img_from_mem(self, url):
         return self.img.copy()
 
-    def get_img_by_time(self, time=None) -> np.array:
+    def get_img_by_time(self, time=None) -> np.ndarray:
         return self.read_single_img_from_mem(self.url)
 
     def get_img_by_idx(self, idx=None):

livecellx/core/single_cell.py

@@ -60,7 +60,7 @@ def __init__(
         ----------
         timeframe : int
             _description_
-        bbox : np.array, optional
+        bbox : np.ndarray, optional
             [x1, y1, x2, y2], by default None
             follwoing skimage convention: "Bounding box (min_row, min_col, max_row, max_col). Pixels belonging to the bounding box are in the half-open interval [min_row; max_row) and [min_col; max_col)."
         regionprops : RegionProperties, optional
@@ -255,7 +255,7 @@ def compute_iomin(self, other_cell: "SingleCellStatic", bbox=None):
     def update_regionprops(self):
         self.regionprops = self.compute_regionprops()
 
-    def get_contour(self) -> np.array:
+    def get_contour(self) -> np.ndarray:
         return np.copy(self.contour)
 
     def get_img(self):
@@ -278,7 +278,7 @@ def get_label_mask(self, dtype=int):
         mask = self.get_mask(dtype=dtype)
         return mask
 
-    def get_bbox(self, padding=None) -> np.array:
+    def get_bbox(self, padding=None) -> np.ndarray:
         # TODO: add unit test for this function
         if self.bbox is None:
             self.update_bbox()
@@ -390,7 +390,7 @@ def update_contour(self, contour, update_bbox=True, update_mask_dataset=True, dt
         if update_bbox:
             self.bbox = self.get_bbox_from_contour(self.contour)
 
-    def update_sc_mask_by_crop(self, mask, padding_pixels=np.zeros(2, dtype=int), bbox: np.array = None):
+    def update_sc_mask_by_crop(self, mask, padding_pixels=np.zeros(2, dtype=int), bbox: np.ndarray = None):
         """
         Updates the single cell mask by cropping the input mask to the bounding box of the single cell and updating the contour.
 
@@ -684,7 +684,7 @@ def get_bbox_on_crop(self, bbox=None, padding=0):
         contours = self.get_contour_coords_on_crop(bbox=bbox, padding=padding)
         return self.get_bbox_from_contour(contours)
 
-    def get_contour_coords_on_img_crop(self, padding=0) -> np.array:
+    def get_contour_coords_on_img_crop(self, padding=0) -> np.ndarray:
         """
         A utility function to calculate pixel coord in image crop's coordinate system
         to draw contours on an image crop.
@@ -703,7 +703,7 @@ def get_contour_coords_on_img_crop(self, padding=0) -> np.array:
         ys = self.contour[:, 1] - max(0, self.bbox[1] - padding)
         return np.array([xs, ys]).T
 
-    def get_contour_mask_closed_form(self, padding=0, crop=True) -> np.array:
+    def get_contour_mask_closed_form(self, padding=0, crop=True) -> np.ndarray:
         """If contour points are pixel-wise closed, use this function to fill the contour."""
         import scipy.ndimage as ndimage
 
@@ -729,7 +729,7 @@ def get_bbox_from_contour(contour, dtype=int):
     @staticmethod
     def gen_contour_mask(
         contour, img=None, shape=None, bbox=None, padding=0, crop=True, mask_val=255, dtype=bool
-    ) -> np.array:  #
+    ) -> np.ndarray:  #
         # TODO: optimize: we do not need img here but shape of img.
         from PIL import Image, ImageDraw
         import numpy as np
@@ -768,7 +768,7 @@ def gen_contour_mask(
     @staticmethod
     def gen_contour_mask_skimage_deprecated(
         contour, img=None, shape=None, bbox=None, padding=0, crop=True, mask_val=255, dtype=bool
-    ) -> np.array:  #
+    ) -> np.ndarray:  #
         # TODO: optimize: we do not need img here but shape of img.
         from skimage.draw import line, polygon
 
@@ -790,7 +790,7 @@ def gen_contour_mask_skimage_deprecated(
         res_mask = SingleCellStatic.gen_skimage_bbox_img_crop(bbox, res_mask, padding=padding)
         return res_mask
 
-    def get_contour_mask(self, padding=0, crop=True, bbox=None, dtype=bool) -> np.array:
+    def get_contour_mask(self, padding=0, crop=True, bbox=None, dtype=bool) -> np.ndarray:
         hash_key: Tuple = ("contour_mask", padding, crop, tuple(bbox if bbox is not None else [-1]))
         if self.enable_cache_contour_mask and hash_key in self.cache:
             return self.cache[hash_key]
@@ -802,7 +802,7 @@ def get_contour_mask(self, padding=0, crop=True, bbox=None, dtype=bool) -> np.ar
             self.cache[hash_key] = res
         return res
 
-    def get_contour_label_mask(self, padding=0, crop=True, bbox=None, dtype=int) -> np.array:
+    def get_contour_label_mask(self, padding=0, crop=True, bbox=None, dtype=int) -> np.ndarray:
         hash_key: Tuple = ("contour_mask", padding, crop, tuple(bbox if bbox is not None else [-1]))
         if self.enable_cache_contour_mask and hash_key in self.cache:
             return self.cache[hash_key]
@@ -814,7 +814,7 @@ def get_contour_label_mask(self, padding=0, crop=True, bbox=None, dtype=int) ->
             self.cache[hash_key] = res
         return res
 
-    def get_contour_img(self, crop=True, bg_val=0, **kwargs) -> np.array:
+    def get_contour_img(self, crop=True, bg_val=0, **kwargs) -> np.ndarray:
         """return a contour image with out of self cell region set to background_val"""
 
         # TODO: filter kwargs for contour mask case. (currently using the same kwargs as self.gen_skimage_bbox_img_crop)

livecellx/core/utils.py

@@ -70,7 +70,7 @@ def rgb_img_to_gray(img):
     return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
 
 
-def get_cv2_bbox(label_mask: np.array):
+def get_cv2_bbox(label_mask: np.ndarray):
     """generate cv2 style bounding box from a label mask
 
     Parameters

livecellx/model_zoo/segmentation/sc_correction_dataset.py

@@ -134,7 +134,7 @@ def __init__(
 
         self.force_no_edt_aug = force_no_edt_aug
 
-    def get_raw_seg(self, idx) -> np.array:
+    def get_raw_seg(self, idx) -> np.ndarray:
         return np.array(Image.open(self.raw_seg_paths[idx]))
 
     def get_scale(self, idx):
@@ -145,7 +145,8 @@ def get_subdir(self, idx):
             return self.subdirs.iloc[idx]
         return self.subdirs[idx]
 
-    def label_mask_to_edt(label_mask: np.array, bg_val=0):
+    @staticmethod
+    def label_mask_to_edt(label_mask: np.ndarray, bg_val=0):
         label_mask = label_mask.astype(np.uint8)
         labels = set(np.unique(label_mask))
         labels.remove(bg_val)
@@ -331,7 +332,7 @@ def get_paths(self, idx):
             "aug_diff_img": self.aug_diff_img_paths[idx] if self.aug_diff_img_paths else None,
         }
 
-    def get_gt_label_mask(self, idx) -> np.array:
+    def get_gt_label_mask(self, idx) -> np.ndarray:
         return np.array(Image.open(self.gt_label_mask_paths[idx]))
 
     def __len__(self):

livecellx/preprocess/augmentor_utils.py

@@ -2,14 +2,14 @@
 import numpy as np
 
 
-def augment_images_by_augmentor(images: np.array, masks: np.array, crop_image_size: int, sampling_amount: int):
+def augment_images_by_augmentor(images: np.ndarray, masks: np.ndarray, crop_image_size: int, sampling_amount: int):
     """Augments images and masks using Augmentor library.
 
     Parameters
     ----------
-    images : np.array
+    images : np.ndarray
         N x w x h x channel
-    masks : np.array
+    masks : np.ndarray
         N x w x h x channel
     crop_image_size :
         size of the cropped image (outputs)

livecellx/preprocess/utils.py

@@ -33,12 +33,12 @@ def normalize_edt(edt_img, edt_max=5):
     return res_img
 
 
-def normalize_features_zscore(features: np.array) -> np.array:
+def normalize_features_zscore(features: np.ndarray) -> np.ndarray:
     """normalize features to z score
 
     Parameters
     ----------
-    features : np.array
+    features : np.ndarray
         _description_
 
     Returns
@@ -53,12 +53,12 @@ def normalize_features_zscore(features: np.array) -> np.array:
     return features
 
 
-def normalize_img_to_uint8(img: np.array, dtype=np.uint8) -> np.array:
+def normalize_img_to_uint8(img: np.ndarray, dtype=np.uint8) -> np.ndarray:
     """calculate z score of img and normalize to range [0, 255]
 
     Parameters
     ----------
-    img : np.array
+    img : np.ndarray
         _description_
 
     Returns
@@ -138,11 +138,11 @@ def overlay_by_color(image, mask, color=(100, 0, 0), alpha=0.5):
 
 
 # TODO: add tests and note if scale * raw_image exceeds type boundaries such as 255
-def reserve_img_by_pixel_percentile(raw_img: np.array, percentile: float, target_val: float = None, scale: float = 1):
+def reserve_img_by_pixel_percentile(raw_img: np.ndarray, percentile: float, target_val: float = None, scale: float = 1):
     """
     Parameters
     ----------
-    raw_img : np.array
+    raw_img : np.ndarray
         _description_
     percentile : float
         _description_
@@ -174,14 +174,14 @@ def reserve_img_by_pixel_percentile(raw_img: np.array, percentile: float, target
     return flattened_img.reshape(raw_img.shape)
 
 
-def enhance_contrast(img: np.array, factor=5):
+def enhance_contrast(img: np.ndarray, factor=5):
     im = Image.fromarray(img)
     enhancer = ImageEnhance.Contrast(im)
     im_output = enhancer.enhance(factor)
     return np.array(im_output)
 
 
-def dilate_or_erode_mask(cropped_mask: np.array, scale_factor):
+def dilate_or_erode_mask(cropped_mask: np.ndarray, scale_factor):
     """
     # TODO ensure reproducibility with different values of padding
     cv's erode and dilation definition:  https://docs.opencv.org/3.4/db/df6/tutorial_erosion_dilatation.html
@@ -203,7 +203,7 @@ def dilate_or_erode_mask(cropped_mask: np.array, scale_factor):
     return s_cropped_mask
 
 
-def dilate_or_erode_label_mask(label_mask: np.array, scale_factor, bg_val=0):
+def dilate_or_erode_label_mask(label_mask: np.ndarray, scale_factor, bg_val=0):
     """Erode label mask to make each labeled region smaller and thus separated."""
     labels = np.unique(label_mask)
     # remove bg label

livecellx/segment/cellpose_utils.py

@@ -2,7 +2,7 @@
 from livecellx.preprocess.utils import normalize_img_to_uint8
 
 
-def segment_single_image_by_cellpose(image, model, channels=[[0, 0]], diameter=150) -> np.array:
+def segment_single_image_by_cellpose(image, model, channels=[[0, 0]], diameter=150) -> np.ndarray:
     result_tuple = model.eval([image], diameter=diameter, channels=channels)
     masks = result_tuple[0]
     return np.array(masks[0])

livecellx/segment/ou_utils.py

@@ -228,7 +228,7 @@ def underseg_overlay_scs(
     return (img_crop, seg_crop, combined_gt_mask)
 
 
-def gen_aug_diff_mask(aug_mask: np.array, combined_gt_mask: np.array, dtype=np.int32) -> np.array:
+def gen_aug_diff_mask(aug_mask: np.ndarray, combined_gt_mask: np.ndarray, dtype=np.int32) -> np.ndarray:
     """generate a mask based on the difference between the augmented mask and the combined gt mask
     0: no difference
     -1: augmented mask is 0, combined gt mask is 1 -> over-segmentation
@@ -237,9 +237,9 @@ def gen_aug_diff_mask(aug_mask: np.array, combined_gt_mask: np.array, dtype=np.i
 
     Parameters
     ----------
-    aug_mask : np.array
+    aug_mask : np.ndarray
         _description_
-    combined_gt_mask : np.array
+    combined_gt_mask : np.ndarray
         _description_
 
     Returns

livecellx/track/sort_tracker_utils.py

@@ -20,7 +20,7 @@ def is_SORT_tracker_result_empty(sort_tracker):
     return sort_tracker.time_since_update is not None
 
 
-def get_bbox_from_contour(contour: list) -> np.array:
+def get_bbox_from_contour(contour: list) -> np.ndarray:
     """get bboxes from a contour
 
     Parameters