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):
# 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)
img_fromarray(np.uint8(img)).save(image)
print("Creating dream video...")
_create_video(video, frame_rate)
print("Dream video created.")
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 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 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", loc="", 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:
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, loc)
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)
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 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):
img = np.float32(img_open(img_path))
s = scale_coefficient
h, w = img.shape[:2]
# Load DNN model
net = Classifier(
NET_FN, PARAM_FN, mean=CAFFE_MEAN, channel_swap=CHANNEL_SWAP)
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)
img_fromarray(np.uint8(img)).save("{}_{}.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)
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 main(img_name,octaves=4,octave_scale=1.4,iterations=10,jitter=32,step_size=1.5,layer='inception_4c/output',gpu=0,go_deeper=50,scale_coefficient=0.05):
path = os.getcwd()
gif_mode = 0
if not os.path.exists(path+"\\gifsicle.exe"):
print "Can't process GIFs, gifsicle.exe not found!"
exit(1)
if os.system("mkdir dreams"): #create a remporary file for image processing/storing
print "temporary output folder already exists, deleting exisisting one.."
os.system("rm -r dreams")
os.system("mkdir dreams")
#Use gifsicle to get frames from the gif and put it in an temporary output folder
if not os.path.exists(img_name):
print "No input file found!"
exit(1)
if img_name[-3:]=='gif':
gif_mode = 1
os.system("gifsicle --explode -U "+img_name+" --output frame")
os.system("ren frame.* frame.*.jpg")
if os.system("move *.jpg dreams"):
print "Can't move files!"
exit(1)
# else,
#go-deeper mode | param required or - default is 50
if gpu:
caffe.set_mode_gpu()
else:
print "You are using CPU mode, this might take some time"
model_path = './caffe/models/bvlc_googlenet/' # substitute your path here
net_fn = model_path + 'deploy.prototxt'
param_fn = model_path + 'bvlc_googlenet.caffemodel'
"""
Other models : ( need to change default end param if you are going to use this )
model_path = './caffe/models/vgg_face_caffe/'
net_fn = model_path + 'VGG_FACE_deploy.prototxt'
param_fn = model_path + 'VGG_FACE.caffemodel'
#(example end params: 'conv1_1','conv1_2','pool1','conv2_1','conv2_2','pool2','conv3_1','conv3_2','conv3_3','pool3','conv4_1','conv4_2','conv4_3','pool4','conv5_1','conv5_2','conv5_3','pool5')
model_path = './caffe/models/finetune_flickr_style/'
net_fn = model_path + 'deploy.prototxt'
param_fn = model_path + 'finetune_flickr_style.caffemodel'
#(example end params: 'conv1','pool1','norm1','conv2','pool2','norm2','conv3','conv4','conv5','pool5')
"""
# Patching model to be able to compute gradients.
# Note that you can also manually add "force_backward: true" line to "deploy.prototxt".
model = caffe.io.caffe_pb2.NetParameter()
text_format.Merge(open(net_fn).read(), model)
model.force_backward = True
open('tmp.prototxt', 'w').write(str(model))
net = caffe.Classifier('tmp.prototxt', param_fn,
mean = np.float32([104.0, 116.0, 122.0]), # ImageNet mean, training set dependent
channel_swap = (2,1,0)) # the reference model has channels in BGR order instead of RGB
if gif_mode:
cnt = 0
dr = path+"\\dreams"
total = len(os.listdir(dr))-1
for i in os.listdir(dr):
if i.endswith(".jpg"):
frame_name = dr+"\\"+i
img = (PIL.Image.open(frame_name))
img = img.convert('RGB')
#print(img.format, img.size, img.mode)
dream_img = deepdream(net, np.array(img),iter_n=iterations,octave_n=octaves,octave_scale=octave_scale,end=layer,jitter=jitter,step_size=step_size)
dream_img = img_fromarray(np.uint8(dream_img)).convert('P', palette=PIL.Image.ADAPTIVE)
dream_img.save(dr+"\\dreamimg"+str(i)+".gif")
os.system("rm dreams\\"+i)
cnt+=1
print str(cnt)+" frames completed out of "+str(total)
else: #go-deeper mode, takes in a single jpg image and dreates dreams of itself.
img = (PIL.Image.open(path+"\\"+img_name))
img = img.convert('RGB')
img = np.array(img)
frame = img
h, w = frame.shape[:2]
s = scale_coefficient # scale coefficient
for i in xrange(go_deeper):
frame = deepdream(net, frame)
PIL.Image.fromarray(np.uint8(frame)).convert('P', palette=PIL.Image.ADAPTIVE).save(path+"\\"+"dreams\\%04d.gif"%i)
frame = nd.affine_transform(frame, [1-s,1-s,1], [h*s/2,w*s/2,0], order=1)
print str(i)+" frames completed out of "+str(go_deeper)
os.system("gifsicle --loop=0 dreams/*.gif > "+img_name[:-4]+"-dream.gif");
os.system("rm -r dreams");
print "File saved as "+img_name[:-4]+"-dream.gif"
print "Done!"
exit(1)
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!!")
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!!")