def main(screen):
# Load logo
logo = app.load_image("player.bmp", -1)
r = image.get_rect()
# blit background on to the screen
# Create a new FaderSurface with the same dimensions and an initial
# transparency of 1.
surface = Complex.FaderSurface(r.width, r.height, 1)
#Blit the original o the FaderSurface.
screen.blit(logo, (0,0))
#The default step value is -1, but we wnat to fade the image in.
surface.step = 1
def reload_image(context): image = context.document.CurrentObject() if image is not None and isinstance(image, external.ExternalGraphics): # Don't try this at home :) It pokes around in the internals of # Sketch! olddata = image.data filename = olddata.Filename() oldrect = image.bounding_rect # first, remove the old object from the cache. if olddata.stored_in_cache \ and external.instance_cache.has_key(filename): del external.instance_cache[filename] olddata.stored_in_cache = 0 # now we can load the data again the normal way because it's not # in the cache anymore. if image.is_Eps: data = eps.load_eps(filename) else: data = app.load_image(filename) # replace the old data object with the new one. Normally we # would have to handle the undo info returned. Here we just # discard it so that the reload won't be in the history. image.SetData(data) # some house keeping tasks that are necessary because the sort # of thing we're doing here, i.e. modifying an object without # undo information etc., wasn't anticipated: # to make sure that the bboxes get recomputed etc, call the # _changed method. SetData should probably do that # automatically, but currently it doesn't image._changed() # make sure the object itself is properly redrawn context.document.AddClearRect(oldrect) context.document.AddClearRect(image.bounding_rect) # make sure the selection's idea of the bounding rect is updated # too and have the canvas update the handles context.document.selection.ResetRectangle() context.main_window.canvas.update_handles()
def image(self, attrs):
if self.in_defs:
return
href = attrs['xlink:href']
image = None
if href[:5] == 'data:':
# embed image
coma = href.find(',')
semicolon = href.find(';')
mime = href[5:semicolon]
if mime in [
'image/png', 'image/jpg', 'image/jpeg', 'image/gif',
'image/bmp'
]:
import base64
image = Image.open(StringIO(base64.decodestring(href[coma:])))
if image.mode == 'P':
image = image.convert('RGBA')
else:
# linked image
import urlparse, urllib
path = urlparse.urlparse(href).path
href = urllib.unquote(path.encode('utf-8'))
path = os.path.realpath(href)
if os.path.isfile(path):
image = load_image(path).image
else:
self.loader.add_message(
_('Cannot find linked image file %s') % path)
if image:
x, y = self.user_point(attrs.get('x', '0'), attrs.get('y', '0'))
width = self.user_length(attrs['width'])
scalex = width / image.size[0]
height = self.user_length(attrs['height'])
scaley = -height / image.size[1]
self.parse_attrs(attrs)
self.set_loader_style()
t = self.trafo(Trafo(scalex, 0, 0, scaley, x, y + height))
self._print('image', t)
self.loader.image(image, t)
def image(self, attrs):
if self.in_defs:
return
href = attrs['xlink:href']
image = None
if href[:5] == 'data:':
# embed image
coma = href.find(',')
semicolon = href.find(';')
mime = href[5:semicolon]
if mime in ['image/png','image/jpg','image/jpeg','image/gif','image/bmp']:
import base64
image = Image.open(StringIO(base64.decodestring(href[coma:])))
if image.mode == 'P':
image = image.convert('RGBA')
else:
# linked image
import urlparse, urllib
path = urlparse.urlparse(href).path
href = urllib.unquote(path.encode('utf-8'))
path = os.path.realpath(href)
if os.path.isfile(path):
image = load_image(path).image
else:
self.loader.add_message(_('Cannot find linked image file %s') % path)
if image:
x, y = self.user_point(attrs.get('x', '0'), attrs.get('y', '0'))
width = self.user_length(attrs['width'])
scalex = width / image.size[0]
height = self.user_length(attrs['height'])
scaley = -height / image.size[1]
self.parse_attrs(attrs)
self.set_loader_style()
t = self.trafo(Trafo(scalex, 0, 0, scaley, x, y + height))
self._print('image', t)
self.loader.image(image, t)
import torch
import torch.optim as optim
from torchvision import transforms, models
import matplotlib.pyplot as plt
from app import load_image, im_convert, get_features, gram_matrix
vgg = models.vgg19(pretrained=True).features
for param in vgg.parameters():
param.requires_grad_(False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
vgg.to(device)
content = load_image('/home/murugesh/PycharmProjects/Style_Transfer/images/5.jpg').to(device)
style = load_image('/home/murugesh/PycharmProjects/Style_Transfer/images/4.jpg', shape=content.shape[-2:]).to(device)
content_features = get_features(content, vgg)
style_features = get_features(style, vgg)
style_grams = {layer: gram_matrix(style_features[layer]) for layer in style_features}
target = content.clone().requires_grad_(True).to(device)
style_weights = {'conv1_1': 1.,
'conv2_1': 0.75,
'conv3_1': 0.2,
'conv4_1': 0.2,
'conv5_1': 0.2}
content_weight = 1
style_weight = 1e6
show_every = 400
