def read_acv(self, name):
print "Reading curves from %s.acv" % name
acv_path = static.path('acv', "%s.acv" % name)
with open(acv_path, "rb") as acv_file:
_, self.count = unpack("!hh", acv_file.read(4))
for i in xrange(self.count):
self.curves.append(
self.read_one_curve(acv_file, self.channel_name(i)))
def read_acv(self, name):
print "Reading curves from %s.acv" % name
acv_path = static.path(
'acv', "%s.acv" % name)
with open(acv_path, "rb") as acv_file:
_, self.count = unpack("!hh", acv_file.read(4))
for i in xrange(self.count):
self.curves.append(
self.read_one_curve(
acv_file, self.channel_name(i)))
self.n = n
if nY is not None:
self.nY = nY
else:
self.nY = n
def process(self, img):
import numpy
from PIL import Image
out = kernels.gaussian_blur_filter(
numpy.array(img),
sigma=self.n)
return Image.fromarray(out)
if __name__ == '__main__':
from PIL import Image
from django_instakit.utils import static
image_paths = map(
lambda image_file: static.path('img', image_file),
static.listfiles('img'))
image_inputs = map(
lambda image_path: Image.open(image_path).convert('RGB'),
image_paths)
for image_input in image_inputs:
image_input.show()
GaussianBlur(n=3).process(image_input).show()
print image_paths
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
if __name__ == '__main__':
from django_instakit.utils import static
image_paths = map(lambda image_file: static.path('img', image_file),
static.listfiles('img'))
image_inputs = map(
lambda image_path: Image.open(image_path).convert('RGB'), image_paths)
for image_input in image_inputs:
image_input.show()
SquareCrop().process(image_input).show()
print image_paths