def deepdream_video(
video, iter_n=10, octave_n=4, octave_scale=1.4,
end="inception_4c/output", clip=True, network="bvlc_googlenet",
frame_rate=24, verbose=False):
# Select, load DNN model
NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network(network)
net = Classifier(
NET_FN, PARAM_FN, mean=CAFFE_MEAN, channel_swap=CHANNEL_SWAP)
print("Extracting video...")
_extract_video(video)
output_dir = _output_video_dir(video)
images = listdir(output_dir)
print("Dreaming...")
for image in images:
image = "{}/{}".format(output_dir, image)
img = np.float32(img_open(image))
img = _deepdream(
net, img, iter_n=iter_n, octave_n=octave_n,
octave_scale=octave_scale, end=end, clip=clip, verbose=verbose)
img_fromarray(np.uint8(img)).save(image)
print("Creating dream video...")
_create_video(video, frame_rate)
print("Dream video created.")
def list_layers(network="bvlc_googlenet"):
# Load DNN model
NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network(network)
net = Classifier(NET_FN,
PARAM_FN,
mean=CAFFE_MEAN,
channel_swap=CHANNEL_SWAP)
net.blobs.keys()
def make_i2v_with_caffe(net_path, param_path, tag_path=None):
mean = np.array([164.76139251, 167.47864617, 181.13838569])
net = Classifier(net_path, param_path, mean=mean, channel_swap=(2, 1, 0))
if tag_path is not None:
tags = json.loads(open(tag_path, 'r').read())
assert (len(tags) == 1539)
return CaffeI2V(net, tags)
else:
return CaffeI2V(net)
def deepdream(
img_path, zoom=True, scale_coefficient=0.05, irange=100, iter_n=10,
octave_n=4, octave_scale=1.4, end="inception_4c/output", clip=True,
network="bvlc_googlenet", gif=False, reverse=False, duration=0.1,
loop=False, gpu=False, gpuid=0, verbose=False):
img = np.float32(img_open(img_path))
s = scale_coefficient
h, w = img.shape[:2]
if gpu:
print("Enabling GPU {}...".format(gpuid))
set_device(gpuid)
set_mode_gpu()
# Select, load DNN model
NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network(network)
net = Classifier(
NET_FN, PARAM_FN, mean=CAFFE_MEAN, channel_swap=CHANNEL_SWAP)
img_pool = [img_path]
# Save settings used in a log file
logging.info((
"{} zoom={}, scale_coefficient={}, irange={}, iter_n={}, "
"octave_n={}, octave_scale={}, end={}, clip={}, network={}, gif={}, "
"reverse={}, duration={}, loop={}").format(
img_path, zoom, scale_coefficient, irange, iter_n, octave_n,
octave_scale, end, clip, network, gif, reverse, duration, loop))
print("Dreaming...")
for i in xrange(irange):
img = _deepdream(
net, img, iter_n=iter_n, octave_n=octave_n,
octave_scale=octave_scale, end=end, clip=clip, verbose=verbose)
img_fromarray(np.uint8(img)).save("{}_{}.jpg".format(
img_path, i))
if gif:
img_pool.append("{}_{}.jpg".format(img_path, i))
print("Dream {} saved.".format(i))
if zoom:
img = affine_transform(
img, [1 - s, 1 - s, 1], [h * s / 2, w * s / 2, 0], order=1)
if gif:
print("Creating gif...")
frames = None
if reverse:
frames = [img_open(f) for f in img_pool[::-1]]
else:
frames = [img_open(f) for f in img_pool]
writeGif(
"{}.gif".format(img_path), frames, duration=duration,
repeat=loop)
print("gif created.")
def make_i2v_with_caffe(net_path, param_path, tag_path=None, threshold_path=None):
mean = np.array([ 164.76139251, 167.47864617, 181.13838569])
net = Classifier(
net_path, param_path, mean=mean, channel_swap=(2, 1, 0))
kwargs = {}
if tag_path is not None:
tags = json.loads(open(tag_path, 'r').read())
assert(len(tags) == 1539)
kwargs['tags'] = tags
if threshold_path is not None:
fscore_threshold = np.load(threshold_path)['threshold']
kwargs['threshold'] = fscore_threshold
return CaffeI2V(net, **kwargs)
def deepdream(
img_path,
zoom=True,
scale_coefficient=0.05,
irange=100,
iter_n=10,
octave_n=4,
octave_scale=1.4,
end="inception_4c/output",
clip=True,
network="bvlc_googlenet",
gif=False,
reverse=False,
duration=0.1,
loop=False,
gpu=False,
gpuid=0,
):
img = np.float32(img_open(img_path))
s = scale_coefficient
h, w = img.shape[:2]
if gpu:
tqdm.write("Enabling GPU {}...".format(gpuid))
set_device(gpuid)
set_mode_gpu()
# Select, load DNN model
NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network(network)
net = Classifier(NET_FN,
PARAM_FN,
mean=CAFFE_MEAN,
channel_swap=CHANNEL_SWAP)
img_pool = [img_path]
# Save settings used in a log file
logging.info(
("{} zoom={}, scale_coefficient={}, irange={}, iter_n={}, "
"octave_n={}, octave_scale={}, end={}, clip={}, network={}, gif={}, "
"reverse={}, duration={}, loop={}").format(
img_path,
zoom,
scale_coefficient,
irange,
iter_n,
octave_n,
octave_scale,
end,
clip,
network,
gif,
reverse,
duration,
loop,
))
tqdm.write("Dreaming... (ctrl-C to wake up early)")
for i in tqdm(
range(irange),
bar_format="{l_bar:>20}{bar:10}{r_bar}",
desc="Total",
position=-1,
unit="dream",
):
img = _deepdream(
net,
img,
iter_n=iter_n,
octave_n=octave_n,
octave_scale=octave_scale,
end=end,
clip=clip,
)
img_fromarray(np.uint8(img)).save("{}_{}_{:03d}.jpg".format(
path.splitext(img_path)[0], re.sub('[\\/:"*?<>|]+', "_", end), i))
if gif:
img_pool.append("{}_{}_{:03d}.jpg".format(
path.splitext(img_path)[0], re.sub('[\\/:"*?<>|]+', "_", end),
i))
tqdm.write("Dream {} of {} saved.".format(i + 1, irange))
if zoom:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
img = affine_transform(img, [1 - s, 1 - s, 1],
[h * s / 2, w * s / 2, 0],
order=1)
if gif:
tqdm.write("Creating gif...")
frames = None
if reverse:
frames = [img_open(f) for f in img_pool[::-1]]
else:
frames = [img_open(f) for f in img_pool]
frames[0].save(
"{}.gif".format(img_path),
save_all=True,
optimize=True,
append_images=frames[1:],
loop=0,
)
tqdm.write("gif created.")
def deepdream(q,
dreamname,
toepath,
img_path,
modegpu=True,
gpudevice=0,
zoom=True,
scale_coefficient=0.05,
irange=5,
iter_n=10,
octave_n=4,
octave_scale=1.4,
end="inception_4c/output",
clip=True,
network="bvlc_googlenet",
gif=False,
reverse=False,
duration=0.1,
loop=False):
#logging.debug('Starting')
if modegpu:
caffe.set_mode_gpu()
caffe.set_device(gpudevice)
print("GPU mode [device id: {}]".format(gpudevice))
print("using GPU, but you'd still better make a cup of coffee")
else:
caffe.set_mode_cpu()
print("using CPU...")
img = np.float32(img_open(toepath + "actualframe.jpg"))
s = scale_coefficient
h, w = img.shape[:2]
# Select, load DNN model
NET_FN, PARAM_FN, CHANNEL_SWAP, CAFFE_MEAN = _select_network(
network, toepath)
net = Classifier(NET_FN,
PARAM_FN,
mean=CAFFE_MEAN,
channel_swap=CHANNEL_SWAP)
img_pool = [
img_path,
]
# Save settings used in a log file
'''
logging.info("{} zoom={}, scale_coefficient={}, irange={}, iter_n={}, octave_n={}, octave_scale={}, end={},"\
"clip={}, network={}, gif={}, reverse={}, duration={}, loop={}".format(
img_path, zoom, scale_coefficient, irange, iter_n, octave_n, octave_scale, end, clip, network, gif, reverse,
duration, loop))
'''
print("Dreaming...")
for i in range(irange):
img = _deepdream(net,
img,
iter_n=iter_n,
octave_n=octave_n,
octave_scale=octave_scale,
end=end,
clip=clip)
img_fromarray(np.uint8(img)).save("{}_{}.jpg".format(
img_path + dreamname, i))
if gif:
img_pool.append("{}_{}.jpg".format(img_path + dreamname, i))
print("Dream layer depth {} saved.".format(i))
print("{}_{}.jpg".format(img_path + dreamname, i))
q.put([i, "{}_{}.jpg".format(img_path + dreamname, i)])
if zoom:
img = affine_transform(img, [1 - s, 1 - s, 1],
[h * s / 2, w * s / 2, 0],
order=1)
if gif:
frames = None
if reverse:
frames = [img_open(f) for f in img_pool[::-1]]
else:
frames = [img_open(f) for f in img_pool]
writeGif("{}.gif".format(img_path),
frames,
duration=duration,
repeat=loop)
print("gif created.")
print("Weak up")
print("¡¡Awake!!")