Move imread and friends to datasets. Fixes #81

Author: lucasb-eyer

DeepFried2/datasets/images.py

@@ -0,0 +1,65 @@
+import numpy as _np
+
+
+try:
+    import cv2 as _cv2
+
+    def imread(fname, dtype=_np.float32):
+        im = _cv2.imread(fname, flags=_cv2.IMREAD_UNCHANGED)
+        if im is None:
+            raise IOError("Couldn't open image file {}".format(fname))
+        return im.astype(dtype)
+
+    def imresize(im, h, w):
+        if im.shape[:2] == (h, w):
+            return im
+
+        # Use AREA interpolation as soon as one dimension gets smaller to avoid moiree.
+        inter = _cv2.INTER_AREA if im.shape[0] < h or im.shape[1] < w else _cv2.INTER_LINEAR
+        return _cv2.resize(im, (w,h), interpolation=inter)
+
+except ImportError:
+
+    try:
+        # This is what scipy's imread does lazily.
+        from PIL import Image as _Image
+
+        def imread(fname, dtype=_np.float32):
+            # This does what CV_LOAD_IMAGE_ANYDEPTH does by default.
+            return _np.array(_Image.open(fname), dtype=dtype)
+
+        def imresize(im, h, w):
+            if im.shape[:2] == (h, w):
+                return im
+
+            # This has problems re-reading a numpy array if it's not 8-bit anymore.
+            assert im.max() > 1, "PIL has problems resizing images after they've been changed to e.g. [0-1] range. Either install OpenCV or resize right after reading the image."
+            img = _Image.fromarray(im.astype(_np.uint8))
+            return _np.array(img.resize((w,h), _Image.BILINEAR), dtype=im.dtype)
+
+    except ImportError:
+
+        def imread(fname, dtype=None):
+            raise ImportError(
+                "Neither OpenCV nor the Python Imaging Library (PIL) is "
+                "installed. Please install either for loading images."
+            )
+
+        def imresize(im, h, w):
+            raise ImportError(
+                "Neither OpenCV nor the Python Imaging Library (PIL) is "
+                "installed. Please install either for resizing images."
+            )
+
+
+def imresizecrop(img, size):
+    assert not isinstance(size, tuple), "For now, `size` needs to be a single integer, i.e. it's being squared."
+
+    # First, resize the smallest side to `size`.
+    img = imresize(img, h=img.shape[0]*size//min(img.shape[:2]),
+                        w=img.shape[1]*size//min(img.shape[:2]))
+
+    # Then, crop-out the central part of the largest side.
+    return img[(img.shape[0]-size)//2:img.shape[0]-(img.shape[0]-size)//2,
+               (img.shape[1]-size)//2:img.shape[1]-(img.shape[1]-size)//2,
+               :]

examples/Pretrained/run_vgg.py

@@ -3,8 +3,9 @@
 import sys, os, json, argparse, collections
 import numpy as np
 import DeepFried2 as df
+from DeepFried2.datasets.images import imread, imresizecrop
 
-from examples.utils import printnow, imread, imresizecrop
+from examples.utils import printnow
 
 
 if __name__ == "__main__":

examples/utils.py

@@ -1,5 +1,4 @@
 import sys as _sys
-import numpy as _np
 
 
 def printnow(fmt, *a, **kw):
@@ -40,71 +39,3 @@ def finish(self):
 
     def make_progressbar(prefix, data_size):
         return SimpleProgressBar(data_size, prefix + ", processed {i} of {tot} ({pct:.2%})")
-
-
-# Reading and resizing images
-#############################
-
-
-try:
-    import cv2 as _cv2
-
-    def imread(fname, dtype=_np.float32):
-        im = _cv2.imread(fname, flags=_cv2.IMREAD_UNCHANGED)
-        if im is None:
-            raise IOError("Couldn't open image file {}".format(fname))
-        return im.astype(dtype)
-
-    def imresize(im, h, w):
-        if im.shape[:2] == (h, w):
-            return im
-
-        # Use AREA interpolation as soon as one dimension gets smaller to avoid moiree.
-        inter = _cv2.INTER_AREA if im.shape[0] < h or im.shape[1] < w else _cv2.INTER_LINEAR
-        return _cv2.resize(im, (w,h), interpolation=inter)
-
-except ImportError:
-
-    try:
-        # This is what scipy's imread does lazily.
-        from PIL import Image as _Image
-
-        def imread(fname, dtype=_np.float32):
-            # This does what CV_LOAD_IMAGE_ANYDEPTH does by default.
-            return _np.array(_Image.open(fname), dtype=dtype)
-
-        def imresize(im, h, w):
-            if im.shape[:2] == (h, w):
-                return im
-
-            # This has problems re-reading a numpy array if it's not 8-bit anymore.
-            assert im.max() > 1, "PIL has problems resizing images after they've been changed to e.g. [0-1] range. Either install OpenCV or resize right after reading the image."
-            img = _Image.fromarray(im.astype(_np.uint8))
-            return _np.array(img.resize((w,h), _Image.BILINEAR), dtype=im.dtype)
-
-    except ImportError:
-
-        def imread(fname, dtype=None):
-            raise ImportError(
-                "Neither OpenCV nor the Python Imaging Library (PIL) is "
-                "installed. Please install either for loading images."
-            )
-
-        def imresize(im, h, w):
-            raise ImportError(
-                "Neither OpenCV nor the Python Imaging Library (PIL) is "
-                "installed. Please install either for resizing images."
-            )
-
-
-def imresizecrop(img, size):
-    assert not isinstance(size, tuple), "For now, `size` needs to be a single integer, i.e. it's being squared."
-
-    # First, resize the smallest side to `size`.
-    img = imresize(img, h=img.shape[0]*size//min(img.shape[:2]),
-                        w=img.shape[1]*size//min(img.shape[:2]))
-
-    # Then, crop-out the central part of the largest side.
-    return img[(img.shape[0]-size)//2:img.shape[0]-(img.shape[0]-size)//2,
-               (img.shape[1]-size)//2:img.shape[1]-(img.shape[1]-size)//2,
-               :]