Generated: Wed 2013-03-13 10:33 CET
Source file: /media/Envs/Envs/filer-gallery/lib/python2.7/site-packages/easy_thumbnails/processors.py
Stats: 0 executed, 107 missed, 6 excluded, 122 ignored
import re
try:
from PIL import Image, ImageChops, ImageFilter
except ImportError:
import Image
import ImageChops
import ImageFilter
from easy_thumbnails import utils
def _compare_entropy(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 = utils.image_entropy(start_slice)
end_entropy = utils.image_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 colorspace(im, bw=False, replace_alpha=False, **kwargs):
"""
Convert images to the correct color space.
A passive option (i.e. always processed) of this method is that all images
(unless grayscale) are converted to RGB colorspace.
This processor should be listed before :func:`scale_and_crop` so palette is
changed before the image is resized.
bw
Make the thumbnail grayscale (not really just black & white).
replace_alpha
Replace any transparency layer with a solid color. For example,
``replace_alpha='#fff'`` would replace the transparency layer with
white.
"""
is_transparent = utils.is_transparent(im)
if bw:
if im.mode in ('L', 'LA'):
return im
if is_transparent:
return im.convert('LA')
else:
return im.convert('L')
if im.mode in ('L', 'RGB'):
return im
if is_transparent:
if im.mode != 'RGBA':
im = im.convert('RGBA')
if not replace_alpha:
return im
base = Image.new('RGBA', im.size, replace_alpha)
base.paste(im)
im = base
return im.convert('RGB')
def autocrop(im, autocrop=False, **kwargs):
"""
Remove any unnecessary whitespace from the edges of the source image.
This processor should be listed before :func:`scale_and_crop` so the
whitespace is removed from the source image before it is resized.
autocrop
Activates the autocrop method for this image.
"""
if autocrop:
bw = im.convert('1')
bw = bw.filter(ImageFilter.MedianFilter)
# White background.
bg = Image.new('1', im.size, 255)
diff = ImageChops.difference(bw, bg)
bbox = diff.getbbox()
if bbox:
im = im.crop(bbox)
return im
def scale_and_crop(im, size, crop=False, upscale=False, **kwargs):
"""
Handle scaling and cropping the source image.
Images can be scaled / cropped against a single dimension by using zero
as the placeholder in the size. For example, ``size=(100, 0)`` will cause
the image to be resized to 100 pixels wide, keeping the aspect ratio of
the source image.
crop
Crop the source image height or width to exactly match the requested
thumbnail size (the default is to proportionally resize the source
image to fit within the requested thumbnail size).
By default, the image is centered before being cropped. To crop from
the edges, pass a comma separated string containing the ``x`` and ``y``
percentage offsets (negative values go from the right/bottom). Some
examples follow:
* ``crop="0,0"`` will crop from the left and top edges.
* ``crop="-10,-0"`` will crop from the right edge (with a 10% offset)
and the bottom edge.
* ``crop=",0"`` will keep the default behavior for the x axis
(horizontally centering the image) and crop from the top edge.
The image can also be "smart cropped" by using ``crop="smart"``. The
image is incrementally cropped down to the requested size by removing
slices from edges with the least entropy.
Finally, you can use ``crop="scale"`` to simply scale the image so that
at least one dimension fits within the size dimensions given (you may
want to use the upscale option too).
upscale
Allow upscaling of the source image during scaling.
"""
source_x, source_y = [float(v) for v in im.size]
target_x, target_y = [float(v) for v in size]
if crop or not target_x or not target_y:
scale = max(target_x / source_x, target_y / source_y)
else:
scale = min(target_x / source_x, target_y / source_y)
# Handle one-dimensional targets.
if not target_x:
target_x = source_x * scale
elif not target_y:
target_y = source_y * scale
if scale < 1.0 or (scale > 1.0 and upscale):
# Resize the image to the target size boundary. Round the scaled
# boundary sizes to avoid floating point errors.
im = im.resize((int(round(source_x * scale)),
int(round(source_y * scale))),
resample=Image.ANTIALIAS)
if crop:
# Use integer values now.
source_x, source_y = im.size
# Difference between new image size and requested size.
diff_x = int(source_x - min(source_x, target_x))
diff_y = int(source_y - min(source_y, target_y))
if diff_x or diff_y:
# Center cropping (default).
halfdiff_x, halfdiff_y = diff_x // 2, diff_y // 2
box = [halfdiff_x, halfdiff_y,
min(source_x, int(target_x) + halfdiff_x),
min(source_y, int(target_y) + halfdiff_y)]
# See if an edge cropping argument was provided.
edge_crop = (isinstance(crop, basestring) and
re.match(r'(?:(-?)(\d+))?,(?:(-?)(\d+))?$', crop))
if edge_crop and filter(None, edge_crop.groups()):
x_right, x_crop, y_bottom, y_crop = edge_crop.groups()
if x_crop:
offset = min(int(target_x) * int(x_crop) // 100, diff_x)
if x_right:
box[0] = diff_x - offset
box[2] = source_x - offset
else:
box[0] = offset
box[2] = source_x - (diff_x - offset)
if y_crop:
offset = min(int(target_y) * int(y_crop) // 100, diff_y)
if y_bottom:
box[1] = diff_y - offset
box[3] = source_y - offset
else:
box[1] = offset
box[3] = source_y - (diff_y - offset)
# See if the image should be "smart cropped".
elif crop == 'smart':
left = top = 0
right, bottom = source_x, source_y
while diff_x:
slice = min(diff_x, max(diff_x // 5, 10))
start = im.crop((left, 0, left + slice, source_y))
end = im.crop((right - slice, 0, right, source_y))
add, remove = _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 = im.crop((0, top, source_x, top + slice))
end = im.crop((0, bottom - slice, source_x, bottom))
add, remove = _compare_entropy(start, end, slice, diff_y)
top += add
bottom -= remove
diff_y = diff_y - add - remove
box = (left, top, right, bottom)
# Finally, crop the image!
if crop != 'scale':
im = im.crop(box)
return im
def filters(im, detail=False, sharpen=False, **kwargs):
"""
Pass the source image through post-processing filters.
sharpen
Sharpen the thumbnail image (using the PIL sharpen filter)
detail
Add detail to the image, like a mild *sharpen* (using the PIL
``detail`` filter).
"""
if detail:
im = im.filter(ImageFilter.DETAIL)
if sharpen:
im = im.filter(ImageFilter.SHARPEN)
return im