def dream_that_image(before, after, layer, seed, filehash, iteration):
# dreaming...
mydebugmsg("Dreaming dream #" + str(iteration))
mydebugmsg("before = [" + before + "]")
mydebugmsg("after = [" + after + "]")
bc = BatCountry(DREAMMODEL)
features = bc.prepare_guide(Image.open(seed), end=layer)
image = bc.dream(np.float32(Image.open(before)),
end=layer,
iter_n=20,
objective_fn=BatCountry.guided_objective,
objective_features=features,
verbose=VERBOSITY)
bc.cleanup()
#
# write the output image to file
#
result = Image.fromarray(np.uint8(image))
result.save(after)
#
# Save both the input image and output image to S3 using the MD5 hash of the original file content as the key name
#
keyname = filehash + ".jpg"
key = beforebucket.new_key(keyname)
key.set_contents_from_filename(before)
key.set_acl('public-read')
mydebugmsg("new key name = [" + keyname + "]")
create_thumbnail(before, keyname, before_thumbnails_bucket)
#
# keyname should look like hashvalue.1.jpg
#
keyname = filehash + "." + str(iteration) + ".jpg"
key = afterbucket.new_key(keyname)
key.set_contents_from_filename(after)
key.set_acl('public-read')
mydebugmsg("new key name = [" + keyname + "]")
create_thumbnail(after, keyname, after_thumbnails_bucket)
photo_after_url = "https://{}.{}/{}".format(after_bucket_name,
s3.server_name(), keyname)
tweet_the_nightmare(photo_after_url)
mydebugmsg("url for tweepy = " + photo_after_url)
mydebugmsg("------------------------------------------")
return
# --image initial_images/clouds.jpg \
# --guide-image initial_images/seed_images/starry_night.jpg \
# --output examples/output/seeded/clouds_and_starry_night.jpg
# import the necessary packages
from batcountry import BatCountry
from PIL import Image
import numpy as np
import argparse
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-b", "--base-model", required=True, help="base model path")
ap.add_argument("-l", "--layer", type=str, default="inception_4c/output",
help="layer of CNN to use")
ap.add_argument("-i", "--image", required=True, help="path to base image")
ap.add_argument("-g", "--guide-image", required=True, help="path to guide image")
ap.add_argument("-o", "--output", required=True, help="path to output image")
args = ap.parse_args()
# we can't stop here...
bc = BatCountry(args.base_model)
features = bc.prepare_guide(Image.open(args.guide_image), end=args.layer)
image = bc.dream(np.float32(Image.open(args.image)), end=args.layer,
iter_n=20, objective_fn=BatCountry.guided_objective,
objective_features=features,)
# write the output image to file
result = Image.fromarray(np.uint8(image))
result.save(args.output)
from batcountry import BatCountry
import numpy as np
from PIL import *
import pdb
bc = BatCountry("/home/dylan/caffe/models/bvlc_googlenet")
features = bc.prepare_guide(Image.open('./guide.jpg'), end='inception_5b/5x5_reduce')
image = bc.dream(np.float32(Image.open('./image.jpg')), end='inception_5b/5x5_reduce',
iter_n=20, objective_fn=BatCountry.guided_objective,
objective_features=features,)
pdb.set_trace()
bc.cleanup()
# --guide-image initial_images/seed_images/starry_night.jpg \
# --output examples/output/seeded/clouds_and_starry_night.jpg
# import the necessary packages
from batcountry import BatCountry
from PIL import Image
import numpy as np
import argparse
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-b", "--base-model", required=True, help="base model path")
ap.add_argument("-l", "--layer", type=str, default="inception_4c/output",
help="layer of CNN to use")
ap.add_argument("-i", "--image", required=True, help="path to base image")
ap.add_argument("-g", "--guide-image", required=True, help="path to guide image")
ap.add_argument("-o", "--output", required=True, help="path to output image")
args = ap.parse_args()
# we can't stop here...
bc = BatCountry(args.base_model)
features = bc.prepare_guide(Image.open(args.guide_image), end=args.layer)
image = bc.dream(np.float32(Image.open(args.image)), end=args.layer,
iter_n=20, objective_fn=BatCountry.guided_objective,
objective_features=features,)
bc.cleanup()
# write the output image to file
result = Image.fromarray(np.uint8(image))
result.save(args.output)
#!/usr/bin/python
import sys
from batcountry import BatCountry
from PIL import Image
import numpy as np
# dream.py <path_to_guide_image> <path_to_source_image> <path_to_save_image>
# ./dream ./guide.jpg ./in.jpg ./out.jpg
guide = sys.argv[1]
imgin = sys.argv[2]
imgout = sys.argv[3]
bc = BatCountry("/opt/caffe/models/bvlc_googlenet")
features = bc.prepare_guide(Image.open(guide))
image = bc.dream(np.float32(Image.open(imgin)),
iter_n=20, objective_fn=BatCountry.guided_objective,
objective_features=features,)
bc.cleanup()
result = Image.fromarray(np.uint8(image))
result.save(imgout)
#!/usr/bin/python
import sys
from batcountry import BatCountry
from PIL import Image
import numpy as np
# dream.py <path_to_guide_image> <path_to_source_image> <path_to_save_image>
# ./dream ./guide.jpg ./in.jpg ./out.jpg
guide = sys.argv[1]
imgin = sys.argv[2]
imgout = sys.argv[3]
bc = BatCountry("/opt/caffe/models/bvlc_googlenet")
features = bc.prepare_guide(Image.open(guide))
image = bc.dream(
np.float32(Image.open(imgin)),
iter_n=20,
objective_fn=BatCountry.guided_objective,
objective_features=features,
)
bc.cleanup()
result = Image.fromarray(np.uint8(image))
result.save(imgout)
# Rename model file so inference script can pick it up
search_dir = args.base_model
files = filter(os.path.isfile, glob.glob(search_dir + "*.caffemodel"))
files.sort(key=lambda x: os.path.getmtime(x))
model_file = files[-1]
shutil.move(model_file, args.base_model+'/bvlc_googlenet.caffemodel')
# we can't stop here...
if args.classtoshow:
bc = BatCountry(args.base_model, deploy_path='/data/model_cache/deploy_class.prototxt')
else:
bc = BatCountry(args.base_model)
for layer in args.layer:
if args.guide:
features = bc.prepare_guide(Image.open(args.guide), end=layer)
image = bc.dream(np.float32(Image.open(args.image)), end=layer,
iter_n=args.iteration_count, objective_fn=BatCountry.guided_objective,
objective_features=features,)
elif args.mixlayer:
mixed_features = bc.prepare_guide(Image.open(args.image), end=args.mixlayer)
image = bc.dream(np.float32(Image.open(args.image)), end=layer, iter_n=args.iteration_count, objective_fn=BatCountry.guided_objective, objective_features=mixed_features, )
elif args.classtoshow:
octaves = [
{
'layer':'loss3/classifier_zzzz',
'iter_n':190,
'start_sigma':2.5,
'end_sigma':0.78,
