Replace np.bool with np.bool_

mrcnn/model.py

@@ -1270,14 +1270,14 @@ def hook(images, augmenter, parents, default):
         # Make augmenters deterministic to apply similarly to images and masks
         det = augmentation.to_deterministic()
         image = det.augment_image(image)
-        # Change mask to np.uint8 because imgaug doesn't support np.bool
+        # Change mask to np.uint8 because imgaug doesn't support bool
         mask = det.augment_image(mask.astype(np.uint8),
                                  hooks=imgaug.HooksImages(activator=hook))
         # Verify that shapes didn't change
         assert image.shape == image_shape, "Augmentation shouldn't change image size"
         assert mask.shape == mask_shape, "Augmentation shouldn't change mask size"
         # Change mask back to bool
-        mask = mask.astype(np.bool)
+        mask = mask.astype(bool)
 
     # Note that some boxes might be all zeros if the corresponding mask got cropped out.
     # and here is to filter them out

mrcnn/utils.py

@@ -531,7 +531,7 @@ def minimize_mask(bbox, mask, mini_shape):
             raise Exception("Invalid bounding box with area of zero")
         # Resize with bilinear interpolation
         m = resize(m, mini_shape)
-        mini_mask[:, :, i] = np.around(m).astype(np.bool)
+        mini_mask[:, :, i] = np.around(m).astype(bool)
     return mini_mask
 
 
@@ -549,7 +549,7 @@ def expand_mask(bbox, mini_mask, image_shape):
         w = x2 - x1
         # Resize with bilinear interpolation
         m = resize(m, (h, w))
-        mask[y1:y2, x1:x2, i] = np.around(m).astype(np.bool)
+        mask[y1:y2, x1:x2, i] = np.around(m).astype(bool)
     return mask
 
 
@@ -569,10 +569,10 @@ def unmold_mask(mask, bbox, image_shape):
     threshold = 0.5
     y1, x1, y2, x2 = bbox
     mask = resize(mask, (y2 - y1, x2 - x1))
-    mask = np.where(mask >= threshold, 1, 0).astype(np.bool)
+    mask = np.where(mask >= threshold, 1, 0).astype(bool)
 
     # Put the mask in the right location.
-    full_mask = np.zeros(image_shape[:2], dtype=np.bool)
+    full_mask = np.zeros(image_shape[:2], dtype=bool)
     full_mask[y1:y2, x1:x2] = mask
     return full_mask
 

samples/balloon/balloon.py

@@ -165,7 +165,7 @@ def load_mask(self, image_id):
 
         # Return mask, and array of class IDs of each instance. Since we have
         # one class ID only, we return an array of 1s
-        return mask.astype(np.bool), np.ones([mask.shape[-1]], dtype=np.int32)
+        return mask.astype(bool), np.ones([mask.shape[-1]], dtype=np.int32)
 
     def image_reference(self, image_id):
         """Return the path of the image."""

samples/coco/coco.py

@@ -262,7 +262,7 @@ def load_mask(self, image_id):
 
         # Pack instance masks into an array
         if class_ids:
-            mask = np.stack(instance_masks, axis=2).astype(np.bool)
+            mask = np.stack(instance_masks, axis=2).astype(bool)
             class_ids = np.array(class_ids, dtype=np.int32)
             return mask, class_ids
         else:

samples/nucleus/nucleus.py

@@ -232,7 +232,7 @@ def load_mask(self, image_id):
         mask = []
         for f in next(os.walk(mask_dir))[2]:
             if f.endswith(".png"):
-                m = skimage.io.imread(os.path.join(mask_dir, f)).astype(np.bool)
+                m = skimage.io.imread(os.path.join(mask_dir, f)).astype(bool)
                 mask.append(m)
         mask = np.stack(mask, axis=-1)
         # Return mask, and array of class IDs of each instance. Since we have
@@ -322,7 +322,7 @@ def rle_decode(rle, shape):
     rle = np.array(rle, dtype=np.int32).reshape([-1, 2])
     rle[:, 1] += rle[:, 0]
     rle -= 1
-    mask = np.zeros([shape[0] * shape[1]], np.bool)
+    mask = np.zeros([shape[0] * shape[1]], bool)
     for s, e in rle:
         assert 0 <= s < mask.shape[0]
         assert 1 <= e <= mask.shape[0], "shape: {}  s {}  e {}".format(shape, s, e)

samples/shapes/train_shapes.ipynb

@@ -278,7 +278,7 @@
     "            occlusion = np.logical_and(occlusion, np.logical_not(mask[:, :, i]))\n",
     "        # Map class names to class IDs.\n",
     "        class_ids = np.array([self.class_names.index(s[0]) for s in shapes])\n",
-    "        return mask.astype(np.bool), class_ids.astype(np.int32)\n",
+    "        return mask.astype(bool), class_ids.astype(np.int32)\n",
     "\n",
     "    def draw_shape(self, image, shape, dims, color):\n",
     "        \"\"\"Draws a shape from the given specs.\"\"\"\n",