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_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 get_png_photo(
png_factor: int = 9
) -> typing.Tuple[typing.Optional[Image], typing.List[str]]:
"""Get image from web camera.
apt-get install fswebcam
"""
img_path = f"/tmp/{uuid.uuid4().hex}.png"
result = subprocess.run([
"/usr/bin/fswebcam",
"-r",
RESOLUTION,
"--no-banner",
"--device",
f"/dev/{DEVICE}",
"--png",
f"{png_factor}",
img_path,
],
capture_output=True,
text=True)
lines = result.stdout.split("\n")
if os.path.exists(img_path):
image = img_open(img_path)
os.remove(img_path)
else:
image = None
return image, lines
def compare_scenes(self, img_data=None):
if img_data is None:
img_data = self.screen
io_img = BytesIO(img_data)
img = img_open(io_img)
for scene in self._scenes.values():
scene.compare(img)
io_img.close()
most_acc_scene = self.most_acc_scene
self.callback_scenes_update(most_acc_scene)
return most_acc_scene
def avatar(user, **kwargs):
#user=a.user.get()
avatar = user.user_avatar.earliest()
return avatar.avatar.url
#with img_open(avatar.avatar.path) as fin:
# return HttpResponse(content=fin.read(), content_type='image/png')
response = HttpResponse(content_type='image/jpeg')
im = img_open(avatar.avatar.path)
im.save(response, 'JPEG')
#print(dir(response))
return b64encode(response.content)
return response.content
'''
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 load_from_maker(self, path_dir: str):
project = Project.open(path_dir)
scenes = {}
for name, scene in project.scenes.items():
scene: MakerSceneModel
io_img = open(scene.img_path, 'rb')
img = img_open(io_img) # type: Image
new_scene = M.SceneModel(self._event)
new_scene.name = scene.name
for feature in scene.features:
new_feature = M.FeatureModel()
new_feature.load_data(**feature.data)
new_feature.img.load_image(img, *feature.rect)
new_scene.features.append(new_feature)
for object_ in scene.objects:
new_object = M.ObjectModel()
new_object.load_data(**object_.data)
new_object.img.load_image(img, *object_.rect)
new_scene.objects.append(new_object)
io_img.close()
scenes[name] = new_scene
self._scenes = scenes
def load_image(self):
self.img = img_open(self.file_name).convert(self._mode_)
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 get_image_last_area() -> Image:
"""Read lasr image.
"""
return img_open(PATH_ACTUAL_IMG)
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!!")
