def main():
# Define arguments
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--image", help="Path to style image")
parser.add_argument("-c", "--content", help="Path to content image")
parser.add_argument("-o", "--output", help="Output directory")
parser.add_argument("-n", "--noise", help="Path to noise image")
parser.add_argument(
"-z",
"--noiseType",
help="Noise type (0 histogram, 1 crappy, 2 less crappy, 3 white)",
type=int,
default=0)
parser.add_argument("-t",
"--iterations",
help="Max number of iterations to run",
type=int)
parser.add_argument("-w",
"--width",
help="Width of output image",
type=int)
parser.add_argument("-l", "--height", help="Height of output", type=int)
parser.add_argument("-p",
"--pyramid",
help="Use multiscale image pyramid",
type=int,
default=0)
parser.add_argument(
"-a",
"--wLayers",
help=
"Array for the weights of each layer for style, should look like 1,0,0.5,2,0 ",
default='1,1')
parser.add_argument(
"-d",
"--wLayersContent",
help=
"Array for the weights of each layer for content, should look like 1,0,0.5,2,0 ",
default='0,0,1')
parser.add_argument(
"-b",
"--wPyramidStyle",
help=
"Array for the weights of each pyramid level, should look like 1,0,0.5,2,0",
default='1,1')
parser.add_argument("-q",
"--betaStyle",
help="Weight for the content loss",
type=float,
default='1')
parser.add_argument(
"-y",
"--wPyramidContent",
help=
"Array for the weights of each pyramid level of the content, should look like 1,0,0.5,2,0",
default='1,1')
args = parser.parse_args()
styleImage = None
if args.output:
output_directory = args.output
else:
output_directory = "result"
if not os.path.exists(output_directory):
os.makedirs(output_directory)
if args.image:
styleImage = utils.load_image_big(args.image, crop=False) # [0, 1)
if args.content:
contentImage = utils.load_image_big(args.content, crop=False) # [0, 1)
else:
print("Image not defined")
return
# Iterations
if args.iterations:
iter = args.iterations
else:
iter = 20
if args.betaStyle:
beta = args.betaStyle
else:
beta = 1
# Dimensions for the output image
if args.width:
width = args.width
else:
width = np.shape(contentImage)[0]
if args.height:
height = args.height
else:
height = np.shape(contentImage)[1]
#Clear the output folder
if not os.path.exists(output_directory):
os.makedirs(output_directory)
try:
filesToRemove = [f for f in os.listdir(output_directory)]
for f in filesToRemove:
if os.path.isfile(output_directory):
os.remove(os.path.join(output_directory, f))
else:
shutil.rmtree(os.path.join(output_directory, f))
#if os.path.isfile(output_directory):
# os.unlink(output_directory)
#elif os.path.isdir(output_directory): shutil.rmtree(output_directory)
except Exception as e:
print(e)
# Define noise
noiseImage = None
if args.noise:
noiseImage = utils.load_image_big(args.noise, crop=False)
else:
if args.noiseType == 0:
noise = utils.his_noise(contentImage,
sizeW=np.shape(contentImage)[0],
sizeH=np.shape(contentImage)[1])
elif args.noiseType == 1:
noise = utils.crappy_noise(width, height)
elif args.noiseType == 2:
noise = utils.less_crappy_noise(
contentImage,
width,
height,
)
else:
noise = utils.white_noise(height, width)
skimage.io.imsave(os.path.join(output_directory, "his_noise.png"),
noise)
noiseImage = noise
print("Noise Image Shape: ", noiseImage.shape)
#If 4-D, the shape is [batch_size, height, width, channels]
noiseImage = noiseImage.reshape(
(1, np.shape(noiseImage)[0], np.shape(noiseImage)[1],
3)).astype("float32")
# Define image
skimage.io.imsave(os.path.join(output_directory, "originStyle.png"),
styleImage)
styleImage = styleImage.reshape(
(1, np.shape(styleImage)[0], np.shape(styleImage)[1], 3))
skimage.io.imsave(os.path.join(output_directory, "originContent.png"),
contentImage)
contentImage = contentImage.reshape(
(1, np.shape(contentImage)[0], np.shape(contentImage)[1], 3))
with open(os.path.join(output_directory, "Parameters.txt"),
"w") as text_file:
a = datetime.datetime.now()
a = a.strftime("%Y-%m-%d %H:%M%:%S")
print(a)
print("Texture Synthesis Experiment", file=text_file)
print("Time = {}".format(a), file=text_file)
for arg in vars(args):
print(arg, "\t\t\t", getattr(args, arg))
print(arg, "\t\t\t", getattr(args, arg), file=text_file)
#Weights for the loss function
weightsLayers = args.wLayers.split(',')
weightsLayers = list(map(float, weightsLayers))
weightsPyramid = args.wPyramidStyle.split(',')
weightsPyramid = list(map(float, weightsPyramid))
weightsPyramidContent = args.wPyramidContent.split(',')
weightsPyramidContent = list(map(float, weightsPyramidContent))
weightsLayersContent = args.wLayersContent.split(',')
weightsLayersContent = list(map(float, weightsLayersContent))
print(weightsLayers)
print('\n')
print(weightsPyramid)
# Run synthesis
#styleImage = np array of style image
#noiseImage = np array of noise
start = time.time()
run_tensorflow(styleImage, noiseImage, contentImage, output_directory,
args.pyramid, weightsLayers, weightsLayersContent,
weightsPyramid, weightsPyramidContent, iter, beta)
elapsed = time.time() - start
with open(os.path.join(output_directory, "Parameters.txt"),
"a") as text_file:
print("\n")
tmp = time.strftime("%H:%M%:%S", time.gmtime(elapsed))
print("Elapsed time = %s" % tmp)
print("Elapsed time = %s" % tmp, file=text_file)
def main():
# Define arguments
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--image", help="Path to image")
parser.add_argument("-o", "--output", help="Output directory")
parser.add_argument("-n", "--noise", help="Path to noise image")
parser.add_argument(
"-z",
"--noiseType",
help="Noise type (0 histogram, 1 crappy, 2 less crappy, 3 white)",
type=int,
default=0)
parser.add_argument("-t",
"--iterations",
help="Max number of iterations to run",
type=int)
parser.add_argument("-w",
"--width",
help="Width of output image",
type=int)
parser.add_argument("-l", "--height", help="Height of output", type=int)
parser.add_argument("-p",
"--pyramid",
help="Use multiscale image pyramid",
type=int,
default=0)
parser.add_argument(
"-a",
"--wLayers",
help=
"Array for the weights of each layer, should look like 1,0,0.5,2,0 ",
default='1,1')
parser.add_argument(
"-b",
"--wPyramid",
help=
"Array for the weights of each pyramid level, should look like 1,0,0.5,2,0",
default='1,1')
args = parser.parse_args()
proc_img = None
if args.output:
output_directory = args.output
else:
output_directory = "result"
if not os.path.exists(output_directory):
os.makedirs(output_directory)
if args.image:
proc_img = utils.load_image_big(args.image, crop=False) # [0, 1)
else:
print("Image not defined")
return
# Iterations
if args.iterations:
iter = args.iterations
else:
iter = 20
# Dimensions for the output image
if args.width:
width = args.width
else:
width = np.shape(proc_img)[0]
if args.height:
height = args.height
else:
height = np.shape(proc_img)[1]
#Clear the output folder
if not os.path.exists(output_directory):
os.makedirs(output_directory)
try:
filesToRemove = [f for f in os.listdir(output_directory)]
for f in filesToRemove:
if os.path.isfile(output_directory):
os.remove(os.path.join(output_directory, f))
else:
shutil.rmtree(os.path.join(output_directory, f))
#if os.path.isfile(output_directory):
# os.unlink(output_directory)
#elif os.path.isdir(output_directory): shutil.rmtree(output_directory)
except Exception as e:
print(e)
# Define noise
init = None
if args.noise:
init = utils.load_image_big(args.noise)
else:
if args.noiseType == 0:
noise = utils.his_noise(proc_img,
sizeW=np.shape(proc_img)[1],
sizeH=np.shape(proc_img)[0])
elif args.noiseType == 1:
noise = utils.crappy_noise(width, height)
elif args.noiseType == 2:
noise = utils.less_crappy_noise(
proc_img,
width,
height,
)
else:
noise = utils.white_noise(height, width)
skimage.io.imsave(os.path.join(output_directory, "his_noise.png"),
noise)
init = noise
#If 4-D, the shape is [batch_size, height, width, channels]
batch2 = init.reshape(
(1, np.shape(init)[0], np.shape(init)[1], 3)).astype("float32")
######tex = tf.Variable(batch2)
# Define image
skimage.io.imsave(os.path.join(output_directory, "origin.png"), proc_img)
batch1 = proc_img.reshape(
(1, np.shape(proc_img)[0], np.shape(proc_img)[1], 3))
######images = tf.cast(tf.Variable(batch1, trainable=False, dtype='float64'), tf.float32)
#original_im_size = 256
#####images_ = [i.reshape((1, np.shape(i)[0], np.shape(i)[1], 3)) for i in create_pyramids(proc_img, args.pyramid)]
#images_ = [tf.cast(tf.Variable(i, trainable=False, dtype="float64", name="inputImage"), tf.float32) for i in images_]
#noises = [tf.Variable(i, name = "noise") for i in [j.reshape((1, np.shape(j)[0], np.shape(j)[1], 3)).astype("float32") for j in create_pyramids(init, args.pyramid)]]
# images_ = [tf.cast(tf.Variable(images_[i], trainable=False, dtype="float64", name="InputImage%s"%str(i)), tf.float32) for i in range(len(images_))]
# tmp = [j.reshape((1, np.shape(j)[0], np.shape(j)[1], 3)).astype("float32") for j in create_pyramids(init, args.pyramid)]
# noises = [tf.Variable(i, name="noise%s"%str(i)) for i in range(len(tmp))]
#noises = [tf.Variable(i) for i in [init.reshape((1, np.shape(init)[0], np.shape(init)[1], 3)).astype("float32") for j in range(args.pyramid + 1)]
with open(os.path.join(output_directory, "Parameters.txt"),
"w") as text_file:
a = datetime.datetime.now()
a = a.strftime("%Y-%m-%d %H:%M%:%S")
print(a)
print("Texture Synthesis Experiment", file=text_file)
print("Time = {}".format(a), file=text_file)
for arg in vars(args):
print(arg, "\t\t\t", getattr(args, arg))
print(arg, "\t\t\t", getattr(args, arg), file=text_file)
#Weights for the loss function
weightsLayers = args.wLayers.split(',')
weightsLayers = list(map(float, weightsLayers))
weightsPyramid = args.wPyramid.split(',')
weightsPyramid = list(map(float, weightsPyramid))
print(weightsLayers)
print('\n')
print(weightsPyramid)
#utils.testGaussian()
# Run synthesis
## TODO ##
## Rename all variables and parameters
#batch1 = np array of target image
#batch2 = np array of noise
run_tensorflow(batch1, batch2, output_directory, args.pyramid,
weightsLayers, weightsPyramid, iter)
exit()
start = time.time()
if args.pyramid > 0:
run_synthesis_pyramid(noises, images_, proc_img, iter,
output_directory, weightsLayers,
weightsPyramid) # Using VGG19
else:
run_synthesis(tex, images, proc_img, iter, output_directory,
weightsLayers) # Using VGG19
elapsed = time.time() - start
with open(os.path.join(output_directory, "Parameters.txt"),
"a") as text_file:
print("\n")
tmp = time.strftime("%H:%M%:%S", time.gmtime(elapsed))
print("Elapsed time = %s" % tmp)
print("Elapsed time = %s" % tmp, file=text_file)