from pyimagesearch.nn.conv import NeuralStyle
from keras.applications import VGG19
SETTINGS = {
'input_path': 'inputs/quan.jpg',
'style_path': 'inputs/red_chalk.jpg',
'output_path': 'output',
'net': VGG19,
'content_layer': 'block4_conv2',
'style_layer': ['block4_conv1', 'block4_conv2', 'block4_conv3', 'block4_conv4'],
'content_weight': 1.0,
'style_weight': 100.0,
'tv_weight': 10.0,
'iterations': 50
}
nn = NeuralStyle(SETTINGS)
nn.transfer()
p = os.path.sep.join(["outputs", p])
# update the settings dictionary
SETTINGS["input_path"] = inputPath
SETTINGS["style_path"] = stylePath
SETTINGS["output_path"] = p
SETTINGS["content_weight"] = grid["content_weight"]
SETTINGS["style_weight"] = grid["style_weight"]
SETTINGS["tv_weight"] = grid["tv_weight"]
# build the key to the completed dictionary
k = "{}_{}_{}".format(inputFilename, styleFilename, param)
# if we have already performed this experiment, skip it
if k in completed.keys():
print("[INFO] skipping: {}".format(k))
continue
# perform neural style transfer with the current settings
print("[INFO] starting: {}".format(k))
ns = NeuralStyle(SETTINGS)
ns.transfer()
# indicate that the transfer completed successfully
completed[k] = True
# write the dictionary back out to disk
print("[INFO] finished: {}".format(k))
f = open("completed.json", "w")
f.write(json.dumps(completed))
f.close()
def main():
"""Run neural style transfer for multiple parameters
"""
# initialize the dictionary of completed runs
completed = {}
# if the completed dictionary exists, load it
if os.path.exists("completed.json"):
completed = json.loads(open("completed.json", "r").read())
# grab the set of example images
image_paths = list(paths.list_images("inputs"))
# loop over the input images
for input_path in image_paths:
for style_path in image_paths:
# if the two paths are equal, ignore them
if input_path == style_path:
continue
# loop over the parameters
for param in PARAMS:
# parse out the content weight, style weight, and total
# variation weight from the string
params = param.split("-")
grid = {
"content_weight": float(params[0].replace("cw_", "")),
"style_weight": float(params[1].replace("sw_", "")),
"tv_weight": float(params[2].replace("tvw_", "")),
}
# parse the filenames
input_filename = input_path[input_path.rfind("/") + 1:]
input_filename = input_filename[:input_filename.rfind(".")]
style_filename = style_path[style_path.rfind("/") + 1:]
style_filename = style_filename[:style_filename.rfind(".")]
# construct the path to the output file
output_filepath = "_".join(
[input_filename, style_filename, param])
output_filepath = "{}.png".format(output_filepath)
output_filepath = os.path.sep.join(
["outputs", output_filepath])
# update the settings dictionary
SETTINGS["input_path"] = input_path
SETTINGS["style_path"] = style_path
SETTINGS["output_path"] = output_filepath
SETTINGS["content_weight"] = grid["content_weight"]
SETTINGS["style_weight"] = grid["style_weight"]
SETTINGS["tv_weight"] = grid["tv_weight"]
# build the key to the completed dictionary
key = "{}_{}_{}".format(input_filename, style_filename, param)
# if we have already performed this experiment, skip it
if key in completed.keys():
print("[INFO] skipping: {}".format(key))
continue
# perform neural style transfer with the current settings
print("[INFO] starting: {}".format(key))
neural_style = NeuralStyle(SETTINGS)
neural_style.transfer()
# indicate that the transfer completed successfully
completed[key] = True
# write the dictionary back out to disk
print("[INFO] finished: {}".format(key))
f = open("completed.json", "w")
f.write(json.dumps(completed))
f.close()
def main():
"""Perform neural style transfer
"""
neural_style = NeuralStyle(SETTINGS)
neural_style.transfer()
"block4_conv2",
"style_layers": [
"block1_conv1", "block2_conv1", "block3_conv1", "block4_conv1",
"block5_conv1"
],
# store the content, style, and total variation weights,
# respectively
"content_weight":
1.0,
"style_weight":
100.0,
"tv_weight":
7.5,
# number of iterations
"iterations":
75,
}
return SETTINGS
for style in styles:
style_path = "circuits/" + style
name = style.split(".")[0]
output_dir = "output/" + name
os.makedir(dir_name)
settings = create_styles(style_path, output_dir)
ns = NeuralStyle(settings)
ns.transfer()