A wrapper for skimage resize() to avoid warnings

skimage generates different warnings depending on the version. This wrapper function calls skimage.tranform.resize() with the right parameter for each version.

mrcnn/model.py

@@ -16,7 +16,6 @@ import logging
 from collections import OrderedDict
 import multiprocessing
 import numpy as np
-import skimage.transform
 import tensorflow as tf
 import keras
 import keras.backend as K
@@ -1071,8 +1070,7 @@ def rpn_bbox_loss_graph(config, target_bbox, rpn_match, rpn_bbox):
     target_bbox = batch_pack_graph(target_bbox, batch_counts,
                                    config.IMAGES_PER_GPU)
 
-    #use smooth_l1_loss() rather than reimplementing here to reduce code duplication
-    loss = smooth_l1_loss(target_bbox,rpn_bbox)
+    loss = smooth_l1_loss(target_bbox, rpn_bbox)
     
     loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0))
     return loss
@@ -1434,15 +1432,14 @@ def build_detection_targets(rpn_rois, gt_class_ids, gt_boxes, gt_masks, config):
             gt_h = gt_y2 - gt_y1
             # Resize mini mask to size of GT box
             placeholder[gt_y1:gt_y2, gt_x1:gt_x2] = \
-                np.round(skimage.transform.resize(
-                    class_mask, (gt_h, gt_w), order=1, mode="constant")).astype(bool)
+                np.round(utils.resize(class_mask, (gt_h, gt_w))).astype(bool)
             # Place the mini batch in the placeholder
             class_mask = placeholder
 
         # Pick part of the mask and resize it
         y1, x1, y2, x2 = rois[i].astype(np.int32)
         m = class_mask[y1:y2, x1:x2]
-        mask = skimage.transform.resize(m, config.MASK_SHAPE, order=1, mode="constant")
+        mask = utils.resize(m, config.MASK_SHAPE)
         masks[i, :, :, class_id] = mask
 
     return rois, roi_gt_class_ids, bboxes, masks

mrcnn/utils.py

@@ -20,6 +20,7 @@ import skimage.transform
 import urllib.request
 import shutil
 import warnings
+from distutils.version import LooseVersion
 
 # URL from which to download the latest COCO trained weights
 COCO_MODEL_URL = "https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5"
@@ -452,9 +453,8 @@ def resize_image(image, min_dim=None, max_dim=None, min_scale=None, mode="square
 
     # Resize image using bilinear interpolation
     if scale != 1:
-        image = skimage.transform.resize(
-            image, (round(h * scale), round(w * scale)),
-            order=1, mode="constant", preserve_range=True)
+        image = resize(image, (round(h * scale), round(w * scale)),
+                       preserve_range=True)
 
     # Need padding or cropping?
     if mode == "square":
@@ -538,7 +538,7 @@ def minimize_mask(bbox, mask, mini_shape):
         if m.size == 0:
             raise Exception("Invalid bounding box with area of zero")
         # Resize with bilinear interpolation
-        m = skimage.transform.resize(m, mini_shape, order=1, mode="constant")
+        m = resize(m, mini_shape)
         mini_mask[:, :, i] = np.around(m).astype(np.bool)
     return mini_mask
 
@@ -556,7 +556,7 @@ def expand_mask(bbox, mini_mask, image_shape):
         h = y2 - y1
         w = x2 - x1
         # Resize with bilinear interpolation
-        m = skimage.transform.resize(m, (h, w), order=1, mode="constant")
+        m = resize(m, (h, w))
         mask[y1:y2, x1:x2, i] = np.around(m).astype(np.bool)
     return mask
 
@@ -576,7 +576,7 @@ def unmold_mask(mask, bbox, image_shape):
     """
     threshold = 0.5
     y1, x1, y2, x2 = bbox
-    mask = skimage.transform.resize(mask, (y2 - y1, x2 - x1), order=1, mode="constant")
+    mask = resize(mask, (y2 - y1, x2 - x1))
     mask = np.where(mask >= threshold, 1, 0).astype(np.bool)
 
     # Put the mask in the right location.
@@ -891,3 +891,27 @@ def denorm_boxes(boxes, shape):
     scale = np.array([h - 1, w - 1, h - 1, w - 1])
     shift = np.array([0, 0, 1, 1])
     return np.around(np.multiply(boxes, scale) + shift).astype(np.int32)
+
+
+def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True,
+           preserve_range=False, anti_aliasing=False, anti_aliasing_sigma=None):
+    """A wrapper for Scikit-Image resize().
+
+    Scikit-Image generates warnings on every call to resize() if it doesn't
+    receive the right parameters. The right parameters depend on the version
+    of skimage. This solves the problem by using different parameters per
+    version. And it provides a central place to control resizing defaults.
+    """
+    if LooseVersion(skimage.__version__) >= LooseVersion("0.14"):
+        # New in 0.14: anti_aliasing. Default it to False for backward
+        # compatibility with skimage 0.13.
+        return skimage.transform.resize(
+            image, output_shape,
+            order=order, mode=mode, cval=cval, clip=clip,
+            preserve_range=preserve_range, anti_aliasing=anti_aliasing,
+            anti_aliasing_sigma=anti_aliasing_sigma)
+    else:
+        return skimage.transform.resize(
+            image, output_shape,
+            order=order, mode=mode, cval=cval, clip=clip,
+            preserve_range=preserve_range)