Move SmartCrop to crop module

Addresses one of the issues raised by in @madisvain in #93

imagekit/processors/crop.py

@@ -1,4 +1,5 @@
 from ..lib import Image, ImageChops, ImageDraw, ImageStat
+from .utils import histogram_entropy
 
 
 class Side(object):
@@ -69,3 +70,70 @@ class TrimBorderColor(object):
             img = crop(img, bbox, self.sides)
 
         return img
+
+
+class SmartCrop(object):
+    """
+    Crop an image 'smartly' -- based on smart crop implementation from easy-thumbnails:
+
+        https://github.com/SmileyChris/easy-thumbnails/blob/master/easy_thumbnails/processors.py#L193
+
+    Smart cropping whittles away the parts of the image with the least entropy.
+
+    """
+
+    def __init__(self, width=None, height=None):
+        self.width = width
+        self.height = height
+
+    def compare_entropy(self, start_slice, end_slice, slice, difference):
+        """
+        Calculate the entropy of two slices (from the start and end of an axis),
+        returning a tuple containing the amount that should be added to the start
+        and removed from the end of the axis.
+
+        """
+        start_entropy = histogram_entropy(start_slice)
+        end_entropy = histogram_entropy(end_slice)
+
+        if end_entropy and abs(start_entropy / end_entropy - 1) < 0.01:
+            # Less than 1% difference, remove from both sides.
+            if difference >= slice * 2:
+                return slice, slice
+            half_slice = slice // 2
+            return half_slice, slice - half_slice
+
+        if start_entropy > end_entropy:
+            return 0, slice
+        else:
+            return slice, 0
+
+    def process(self, img):
+        source_x, source_y = img.size
+        diff_x = int(source_x - min(source_x, self.width))
+        diff_y = int(source_y - min(source_y, self.height))
+        left = top = 0
+        right, bottom = source_x, source_y
+
+        while diff_x:
+            slice = min(diff_x, max(diff_x // 5, 10))
+            start = img.crop((left, 0, left + slice, source_y))
+            end = img.crop((right - slice, 0, right, source_y))
+            add, remove = self.compare_entropy(start, end, slice, diff_x)
+            left += add
+            right -= remove
+            diff_x = diff_x - add - remove
+
+        while diff_y:
+            slice = min(diff_y, max(diff_y // 5, 10))
+            start = img.crop((0, top, source_x, top + slice))
+            end = img.crop((0, bottom - slice, source_x, bottom))
+            add, remove = self.compare_entropy(start, end, slice, diff_y)
+            top += add
+            bottom -= remove
+            diff_y = diff_y - add - remove
+
+        box = (left, top, right, bottom)
+        img = img.crop(box)
+
+        return img

imagekit/processors/resize.py

@@ -1,6 +1,6 @@
-import math
-
 from imagekit.lib import Image
+from .crop import SmartCrop as _SmartCrop
+import warnings
 
 
 class Crop(object):
@@ -118,84 +118,9 @@ class Fit(object):
         return img
 
 
-def histogram_entropy(im):
-    """
-    Calculate the entropy of an images' histogram. Used for "smart cropping" in easy-thumbnails;
-    see: https://raw.github.com/SmileyChris/easy-thumbnails/master/easy_thumbnails/utils.py
-
-    """
-    if not isinstance(im, Image.Image):
-        return 0  # Fall back to a constant entropy.
-
-    histogram = im.histogram()
-    hist_ceil = float(sum(histogram))
-    histonorm = [histocol / hist_ceil for histocol in histogram]
-
-    return -sum([p * math.log(p, 2) for p in histonorm if p != 0])
-
-
-class SmartCrop(object):
-    """
-    Crop an image 'smartly' -- based on smart crop implementation from easy-thumbnails:
-
-        https://github.com/SmileyChris/easy-thumbnails/blob/master/easy_thumbnails/processors.py#L193
-
-    Smart cropping whittles away the parts of the image with the least entropy.
-
-    """
-
-    def __init__(self, width=None, height=None):
-        self.width = width
-        self.height = height
-
-    def compare_entropy(self, start_slice, end_slice, slice, difference):
-        """
-        Calculate the entropy of two slices (from the start and end of an axis),
-        returning a tuple containing the amount that should be added to the start
-        and removed from the end of the axis.
-
-        """
-        start_entropy = histogram_entropy(start_slice)
-        end_entropy = histogram_entropy(end_slice)
-
-        if end_entropy and abs(start_entropy / end_entropy - 1) < 0.01:
-            # Less than 1% difference, remove from both sides.
-            if difference >= slice * 2:
-                return slice, slice
-            half_slice = slice // 2
-            return half_slice, slice - half_slice
-
-        if start_entropy > end_entropy:
-            return 0, slice
-        else:
-            return slice, 0
-
-    def process(self, img):
-        source_x, source_y = img.size
-        diff_x = int(source_x - min(source_x, self.width))
-        diff_y = int(source_y - min(source_y, self.height))
-        left = top = 0
-        right, bottom = source_x, source_y
-
-        while diff_x:
-            slice = min(diff_x, max(diff_x // 5, 10))
-            start = img.crop((left, 0, left + slice, source_y))
-            end = img.crop((right - slice, 0, right, source_y))
-            add, remove = self.compare_entropy(start, end, slice, diff_x)
-            left += add
-            right -= remove
-            diff_x = diff_x - add - remove
-
-        while diff_y:
-            slice = min(diff_y, max(diff_y // 5, 10))
-            start = img.crop((0, top, source_x, top + slice))
-            end = img.crop((0, bottom - slice, source_x, bottom))
-            add, remove = self.compare_entropy(start, end, slice, diff_y)
-            top += add
-            bottom -= remove
-            diff_y = diff_y - add - remove
-
-        box = (left, top, right, bottom)
-        img = img.crop(box)
-
-        return img
+class SmartCrop(_SmartCrop):
+    def __init__(self, *args, **kwargs):
+        warnings.warn('The SmartCrop processor has been moved to'
+                ' `imagekit.processors.crop.SmartCrop`, where it belongs.',
+                DeprecationWarning)
+        super(SmartCrop, self).__init__(*args, **kwargs)

imagekit/processors/utils.py

@@ -0,0 +1,18 @@
+import math
+from imagekit.lib import Image
+
+
+def histogram_entropy(im):
+    """
+    Calculate the entropy of an images' histogram. Used for "smart cropping" in easy-thumbnails;
+    see: https://raw.github.com/SmileyChris/easy-thumbnails/master/easy_thumbnails/utils.py
+
+    """
+    if not isinstance(im, Image.Image):
+        return 0  # Fall back to a constant entropy.
+
+    histogram = im.histogram()
+    hist_ceil = float(sum(histogram))
+    histonorm = [histocol / hist_ceil for histocol in histogram]
+
+    return -sum([p * math.log(p, 2) for p in histonorm if p != 0])

tests/core/tests.py

@@ -11,7 +11,8 @@ from imagekit import utils
 from imagekit.lib import Image
 from imagekit.models import ImageSpec
 from imagekit.processors import Adjust
-from imagekit.processors.resize import Crop, SmartCrop
+from imagekit.processors.resize import Crop
+from imagekit.processors.crop import SmartCrop
 
 
 class Photo(models.Model):