fix: fixing the numpy dtype warnings > v1.19 (#117)

* ci: fixing the numpy dtype warnings > v1.19

* ci: fixing the numpy dtype warnings > v1.19

* Merge branch 'master' into chore/wadim/np_warnings

Author: wadimkehl

dgp/datasets/base_dataset.py

@@ -363,7 +363,7 @@ def annotation_index(self):
             total_annotations += list(self.autolabeled_scenes.keys())
 
         scene_annotation_index = xr.DataArray(
-            np.zeros((len(self.data), len(total_annotations)), dtype=np.bool),
+            np.zeros((len(self.data), len(total_annotations)), dtype=bool),
             dims=["datums", "annotations"],
             coords={"annotations": total_annotations}
         )

dgp/datasets/frame_dataset.py

@@ -20,7 +20,7 @@
 from dgp.datasets import BaseDataset, DatasetMetadata
 
 SUPPORTED_ANNOTATIONS_TABLE = xr.DataArray(
-    np.zeros((len(DATUM_TYPE_TO_SUPPORTED_ANNOTATION_TYPE), len(ALL_ANNOTATION_TYPES)), dtype=np.bool),
+    np.zeros((len(DATUM_TYPE_TO_SUPPORTED_ANNOTATION_TYPE), len(ALL_ANNOTATION_TYPES)), dtype=bool),
     dims=["datum_types", "annotations"],
     coords={
         "datum_types": list(DATUM_TYPE_TO_SUPPORTED_ANNOTATION_TYPE),

dgp/utils/camera.py

@@ -334,7 +334,7 @@ def in_frustum(self, X, height, width):
             Bool array for X which are inside frustum
         """
         if not len(X):
-            return np.array([], dtype=np.bool)
+            return np.array([], dtype=bool)
 
         corners = np.asarray([(0, 0), (width - 1, 0), (width - 1, height - 1), (0, height - 1)], dtype=np.float32)
         rays = self.unproject(corners)

dgp/utils/structures/instance_mask.py

@@ -13,7 +13,7 @@ class InstanceMask2D():
 
     Parameters
     ----------
-    mask: np.ndarray[np.bool, np.uint8, np.int64]
+    mask: np.ndarray[bool, np.uint8, np.int64]
         2D boolean array describiing instance mask.
 
     class_id: int, default: GENERIC_OBJECT_CLASS
@@ -28,7 +28,7 @@ class InstanceMask2D():
         defaults to empty dict.
     """
     def __init__(self, mask, class_id=GENERIC_OBJECT_CLASS, instance_id=None, color=(0, 0, 0), attributes=None):
-        assert mask.dtype in (np.bool, np.uint8, np.int64)
+        assert mask.dtype in (bool, np.uint8, np.int64)
         self._bitmask = mask
 
         self._class_id = class_id
@@ -120,7 +120,7 @@ class RLEMask():
 
     Parameters
     ----------
-    size: list[int] or np.ndarray[np.int]
+    size: list[int] or np.ndarray[int]
         Height and width of mask.
     counts: list[int]
         Count-encoding of RLE format.

dgp/utils/visualization_utils.py

@@ -99,7 +99,7 @@ def mosaic(items, scale=1.0, pad=3, grid_width=None):
     N = len(items)
     assert N > 0, 'No items to mosaic!'
     grid_width = grid_width if grid_width else np.ceil(np.sqrt(N)).astype(int)
-    grid_height = np.ceil(N * 1. / grid_width).astype(np.int)
+    grid_height = np.ceil(N * 1.0 / grid_width).astype(int)
     input_size = items[0].shape[:2]
     target_shape = (int(input_size[1] * scale), int(input_size[0] * scale))
     mosaic_items = []
@@ -340,7 +340,7 @@ def clip_norm(v, x):
             v_2d = row_tail - row
             v_2d = clip_norm(v_2d, velocity_max_pix * W)
             cx, cy = row
-            cx2, cy2 = row + v_2d.astype(np.int)
+            cx2, cy2 = row + v_2d.astype(int)
             cx2 = np.clip(cx2, 0, W - 1)
             cy2 = np.clip(cy2, 0, H - 1)
             cv2.arrowedLine(img, (cx, cy), (cx2, cy2), color, thickness=2, line_type=cv2.LINE_AA)
@@ -552,7 +552,7 @@ def clip_norm(v, x):
                 v_2d = clip_norm(v_2d, velocity_max_pix * W)
 
                 cx, cy = row
-                cx2, cy2 = row + v_2d.astype(np.int)
+                cx2, cy2 = row + v_2d.astype(int)
 
                 cx2 = np.clip(cx2, 0, W - 1)
                 cy2 = np.clip(cy2, 0, H - 1)