def blurthday():
from imread import imread
from pprint import pprint
imfuckingshowalready = lambda mx: Image.fromarray(mx).show()
identity = LUT()
amatorka = LUT('amatorka')
#miss_etikate = LUT('miss_etikate')
#soft_elegance_1 = LUT('soft_elegance_1')
#soft_elegance_2 = LUT('soft_elegance_2')
im1 = imread(static.path(join('img', '06-DSCN4771.JPG')))
im2 = imread(
static.path(
join('img',
'430023_3625646599363_1219964362_3676052_834528487_n.jpg')))
pprint(identity)
pprint(amatorka)
im9 = amatorka.transform(im1)
pprint(im9)
imfuckingshowalready(im9)
print im1
print im2
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)))
def blurthday():
from imread import imread
from pprint import pprint
imfuckingshowalready = lambda mx: Image.fromarray(mx).show()
identity = LUT()
amatorka = LUT('amatorka')
#miss_etikate = LUT('miss_etikate')
#soft_elegance_1 = LUT('soft_elegance_1')
#soft_elegance_2 = LUT('soft_elegance_2')
im1 = imread(static.path(join('img', '06-DSCN4771.JPG')))
im2 = imread(static.path(join(
'img', '430023_3625646599363_1219964362_3676052_834528487_n.jpg')))
pprint(identity)
pprint(amatorka)
im9 = amatorka.transform(im1)
pprint(im9)
imfuckingshowalready(im9)
print im1
print im2
def old_main():
#imfuckingshowalready = lambda mx: Image.fromarray(mx).show()
old_identity = static.path(join('lut', 'identity.png'))
im_old_identity = imread.imread(old_identity)
im_identity = numpy.zeros_like(im_old_identity)
for bx in xrange(0, 8):
for by in xrange(0, 8):
for r in xrange(0, 64):
for g in xrange(0, 64):
im_identity[
int(g + by * 64),
int(r + bx * 64)] = numpy.array((
int(r * 255.0 / 63.0 + 0.5),
int(g * 255.0 / 63.0 + 0.5),
int((bx + by * 8.0) * 255.0 / 63.0 + 0.5)),
dtype=numpy.uint8)
print "THE OLD: %s, %s, %s" % (
im_old_identity.size, im_old_identity.shape,
str(im_old_identity.dtype))
#print im_old_identity
print ""
print "THE NEW: %s, %s, %s" % (
im_identity.size, im_identity.shape,
str(im_identity.dtype))
#print im_identity
print ""
print "THE END: %s" % bool(im_old_identity.shape == im_identity.shape)
#print im_old_identity == im_identity
#imfuckingshowalready(im_identity)
#imfuckingshowalready(im_old_identity)
pil_im_old_identity = Image.fromarray(im_old_identity)
pil_im_old_identity.save('/tmp/im_old_identity.jpg',
format="JPEG")
pil_im_identity = Image.fromarray(im_identity)
pil_im_identity.save('/tmp/im_identity.jpg',
format="JPEG")
def old_main():
#imfuckingshowalready = lambda mx: Image.fromarray(mx).show()
old_identity = static.path(join('lut', 'identity.png'))
im_old_identity = imread.imread(old_identity)
im_identity = numpy.zeros_like(im_old_identity)
for bx in xrange(0, 8):
for by in xrange(0, 8):
for r in xrange(0, 64):
for g in xrange(0, 64):
im_identity[int(g + by * 64),
int(r + bx * 64)] = numpy.array(
(int(r * 255.0 / 63.0 + 0.5),
int(g * 255.0 / 63.0 + 0.5),
int((bx + by * 8.0) * 255.0 / 63.0 +
0.5)),
dtype=numpy.uint8)
print "THE OLD: %s, %s, %s" % (im_old_identity.size, im_old_identity.shape,
str(im_old_identity.dtype))
#print im_old_identity
print ""
print "THE NEW: %s, %s, %s" % (im_identity.size, im_identity.shape,
str(im_identity.dtype))
#print im_identity
print ""
print "THE END: %s" % bool(im_old_identity.shape == im_identity.shape)
#print im_old_identity == im_identity
#imfuckingshowalready(im_identity)
#imfuckingshowalready(im_old_identity)
pil_im_old_identity = Image.fromarray(im_old_identity)
pil_im_old_identity.save('/tmp/im_old_identity.jpg', format="JPEG")
pil_im_identity = Image.fromarray(im_identity)
pil_im_identity.save('/tmp/im_identity.jpg', format="JPEG")
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 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()
Contour().process(image_input).show()
Detail().process(image_input).show()
Emboss().process(image_input).show()
EdgeEnhance().process(image_input).show()
EdgeEnhanceMore().process(image_input).show()
FindEdges().process(image_input).show()
Smooth().process(image_input).show()
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 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()
Contour().process(image_input).show()
Detail().process(image_input).show()
Emboss().process(image_input).show()
EdgeEnhance().process(image_input).show()
EdgeEnhanceMore().process(image_input).show()
FindEdges().process(image_input).show()
Smooth().process(image_input).show()
SmoothMore().process(image_input).show()
def _read_png_matrix(cls, name):
print "Reading LUT image: %s" % static.path(
join('lut', '%s.png' % name))
return imread.imread(static.path(join('lut', '%s.png' % name)))
def _read_png_matrix(cls, name):
print "Reading LUT image: %s" % static.path(join('lut', '%s.png' % name))
return imread.imread(
static.path(join('lut', '%s.png' % name)))
class SaltAndPepperNoise(Noise):
""" Add 'salt and pepper noise' -- replace random pixel values with 1.0f (255) or zero """
mode = 's&p'
class SpeckleNoise(Noise):
""" Add multiplicative noise using out = image + n*image
(where n is uniform noise with specified mean & variance) """
mode = 'speckle'
if __name__ == '__main__':
from PIL import Image
from 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)
noises = [
GaussianNoise, PoissonNoise, GaussianLocalVarianceNoise,
SaltNoise, PepperNoise, SaltAndPepperNoise, SpeckleNoise
]
for idx, image_input in enumerate(image_inputs + image_inputs[:2]):
image_input.show()
#Noise().process(image_input).show()
noises[idx]().process(image_input).show()
