├── .gitignore ├── LICENSE ├── MANIFEST ├── README.md ├── batcountry ├── __init__.py └── batcountry.py ├── demo.py ├── demo_bulk.py ├── demo_guided.py ├── demo_vis.py ├── docs └── images │ ├── clouds_and_antelope_example.jpg │ └── clouds_and_starry_night_example.jpg ├── examples ├── output │ ├── fear_and_loathing │ │ ├── conv2_3x3_fal_01.jpg │ │ ├── conv2_3x3_fal_02.jpg │ │ ├── conv2_3x3_fal_03.jpg │ │ ├── conv2_3x3_fal_04.jpg │ │ ├── inception_3b_5x5_reduce_fal_01.jpg │ │ ├── inception_3b_5x5_reduce_fal_02.jpg │ │ ├── inception_3b_5x5_reduce_fal_03.jpg │ │ ├── inception_3b_5x5_reduce_fal_04.jpg │ │ ├── inception_4c_output_fal_01.jpg │ │ ├── inception_4c_output_fal_02.jpg │ │ ├── inception_4c_output_fal_03.jpg │ │ └── inception_4c_output_fal_04.jpg │ ├── jurassic_park │ │ ├── conv2_3x3_jp_01.jpg │ │ ├── conv2_3x3_jp_02.jpg │ │ ├── conv2_3x3_jp_03.jpg │ │ ├── conv2_3x3_jp_04.jpg │ │ ├── conv2_3x3_jp_05.jpg │ │ ├── conv2_3x3_jp_06.jpg │ │ ├── inception_3b_5x5_reduce_jp_01.jpg │ │ ├── inception_3b_5x5_reduce_jp_02.jpg │ │ ├── inception_3b_5x5_reduce_jp_03.jpg │ │ ├── inception_3b_5x5_reduce_jp_04.jpg │ │ ├── inception_3b_5x5_reduce_jp_05.jpg │ │ ├── inception_3b_5x5_reduce_jp_06.jpg │ │ ├── inception_4c_output_jp_01.jpg │ │ ├── inception_4c_output_jp_02.jpg │ │ ├── inception_4c_output_jp_03.jpg │ │ ├── inception_4c_output_jp_04.jpg │ │ ├── inception_4c_output_jp_05.jpg │ │ └── inception_4c_output_jp_06.jpg │ ├── seeded │ │ ├── clouds_and_antelope.jpg │ │ └── clouds_and_starry_night.jpg │ ├── simple │ │ ├── simple_fal.jpg │ │ ├── simple_jp.jpg │ │ └── simple_matrix.jpg │ ├── the_matrix │ │ ├── conv2_3x3_matrix_01.jpg │ │ ├── conv2_3x3_matrix_02.jpg │ │ ├── conv2_3x3_matrix_03.jpg │ │ ├── conv2_3x3_matrix_04.jpg │ │ ├── inception_3b_5x5_reduce_matrix_01.jpg │ │ ├── inception_3b_5x5_reduce_matrix_02.jpg │ │ ├── inception_3b_5x5_reduce_matrix_03.jpg │ │ ├── inception_3b_5x5_reduce_matrix_04.jpg │ │ ├── inception_4c_output_matrix_01.jpg │ │ ├── inception_4c_output_matrix_02.jpg │ │ ├── inception_4c_output_matrix_03.jpg │ │ └── inception_4c_output_matrix_04.jpg │ └── visualizations │ │ ├── octave_0-iter_0-layer_conv2_3x3.jpg │ │ ├── octave_0-iter_1-layer_conv2_3x3.jpg │ │ ├── octave_0-iter_2-layer_conv2_3x3.jpg │ │ ├── octave_0-iter_3-layer_conv2_3x3.jpg │ │ ├── octave_0-iter_4-layer_conv2_3x3.jpg │ │ ├── octave_0-iter_5-layer_conv2_3x3.jpg │ │ ├── octave_0-iter_6-layer_conv2_3x3.jpg │ │ ├── octave_0-iter_7-layer_conv2_3x3.jpg │ │ ├── octave_0-iter_8-layer_conv2_3x3.jpg │ │ ├── octave_0-iter_9-layer_conv2_3x3.jpg │ │ ├── octave_1-iter_0-layer_conv2_3x3.jpg │ │ ├── octave_1-iter_1-layer_conv2_3x3.jpg │ │ ├── octave_1-iter_2-layer_conv2_3x3.jpg │ │ ├── octave_1-iter_3-layer_conv2_3x3.jpg │ │ ├── octave_1-iter_4-layer_conv2_3x3.jpg │ │ ├── octave_1-iter_5-layer_conv2_3x3.jpg │ │ ├── octave_1-iter_6-layer_conv2_3x3.jpg │ │ ├── octave_1-iter_7-layer_conv2_3x3.jpg │ │ ├── octave_1-iter_8-layer_conv2_3x3.jpg │ │ ├── octave_1-iter_9-layer_conv2_3x3.jpg │ │ ├── octave_2-iter_0-layer_conv2_3x3.jpg │ │ ├── octave_2-iter_1-layer_conv2_3x3.jpg │ │ ├── octave_2-iter_2-layer_conv2_3x3.jpg │ │ ├── octave_2-iter_3-layer_conv2_3x3.jpg │ │ ├── octave_2-iter_4-layer_conv2_3x3.jpg │ │ ├── octave_2-iter_5-layer_conv2_3x3.jpg │ │ ├── octave_2-iter_6-layer_conv2_3x3.jpg │ │ ├── octave_2-iter_7-layer_conv2_3x3.jpg │ │ ├── octave_2-iter_8-layer_conv2_3x3.jpg │ │ ├── octave_2-iter_9-layer_conv2_3x3.jpg │ │ ├── octave_3-iter_0-layer_conv2_3x3.jpg │ │ ├── octave_3-iter_1-layer_conv2_3x3.jpg │ │ ├── octave_3-iter_2-layer_conv2_3x3.jpg │ │ ├── octave_3-iter_3-layer_conv2_3x3.jpg │ │ ├── octave_3-iter_4-layer_conv2_3x3.jpg │ │ ├── octave_3-iter_5-layer_conv2_3x3.jpg │ │ ├── octave_3-iter_6-layer_conv2_3x3.jpg │ │ ├── octave_3-iter_7-layer_conv2_3x3.jpg │ │ ├── octave_3-iter_8-layer_conv2_3x3.jpg │ │ └── octave_3-iter_9-layer_conv2_3x3.jpg ├── run_bulk.sh └── run_simple.sh ├── initial_images ├── antelope.jpg ├── clouds.jpg ├── fear_and_loathing │ ├── fal_01.jpg │ ├── fal_02.jpg │ ├── fal_03.jpg │ └── fal_04.jpg ├── jurassic_park │ ├── jp_01.jpg │ ├── jp_02.jpg │ ├── jp_03.jpg │ ├── jp_04.jpg │ ├── jp_05.jpg │ └── jp_06.jpg ├── seed_images │ └── starry_night.jpg └── the_matrix │ ├── matrix_01.jpg │ ├── matrix_02.jpg │ ├── matrix_03.jpg │ └── matrix_04.jpg ├── requirements.txt ├── setup.cfg └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | tmp.prototxt 2 | 3 | # Created by https://www.gitignore.io 4 | 5 | ### Linux ### 6 | *~ 7 | 8 | # KDE directory preferences 9 | .directory 10 | 11 | # Linux trash folder which might appear on any partition or disk 12 | .Trash-* 13 | 14 | 15 | ### Python ### 16 | # Byte-compiled / optimized / DLL files 17 | __pycache__/ 18 | *.py[cod] 19 | 20 | # C extensions 21 | *.so 22 | 23 | # Distribution / packaging 24 | .Python 25 | env/ 26 | build/ 27 | develop-eggs/ 28 | dist/ 29 | downloads/ 30 | eggs/ 31 | .eggs/ 32 | lib/ 33 | lib64/ 34 | parts/ 35 | sdist/ 36 | var/ 37 | *.egg-info/ 38 | .installed.cfg 39 | *.egg 40 | 41 | # PyInstaller 42 | # Usually these files are written by a python script from a template 43 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 44 | *.manifest 45 | *.spec 46 | 47 | # Installer logs 48 | pip-log.txt 49 | pip-delete-this-directory.txt 50 | 51 | # Unit test / coverage reports 52 | htmlcov/ 53 | .tox/ 54 | .coverage 55 | .coverage.* 56 | .cache 57 | nosetests.xml 58 | coverage.xml 59 | *,cover 60 | 61 | # Translations 62 | *.mo 63 | *.pot 64 | 65 | # Django stuff: 66 | *.log 67 | 68 | # Sphinx documentation 69 | docs/_build/ 70 | 71 | # PyBuilder 72 | target/ 73 | 74 | ### OSX ### 75 | .DS_Store 76 | .AppleDouble 77 | .LSOverride 78 | 79 | # Icon must end with two \r 80 | Icon 81 | 82 | 83 | # Thumbnails 84 | ._* 85 | 86 | # Files that might appear in the root of a volume 87 | .DocumentRevisions-V100 88 | .fseventsd 89 | .Spotlight-V100 90 | .TemporaryItems 91 | .Trashes 92 | .VolumeIcon.icns 93 | 94 | # Directories potentially created on remote AFP share 95 | .AppleDB 96 | .AppleDesktop 97 | Network Trash Folder 98 | Temporary Items 99 | .apdisk 100 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | The MIT License (MIT) 2 | 3 | Copyright (c) 2015 Adrian Rosebrock 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | 23 | -------------------------------------------------------------------------------- /MANIFEST: -------------------------------------------------------------------------------- 1 | # file GENERATED by distutils, do NOT edit 2 | setup.cfg 3 | setup.py 4 | batcountry/__init__.py 5 | batcountry/batcountry.py 6 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # bat-country 2 |
3 | 4 | > We can't stop here, this is bat country. — Raoul Duke 5 | 6 | The `bat-country` package is an **easy to use**, **highly extendible**, **lightweight Python module** for **inceptionism** and **deep dreaming** with Convolutional Neural Networks and Caffe. My contributions here are honestly pretty minimal. All the real research has been done by the [Google Research Team](http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html) — I'm simply taking the [IPython Notebook](https://github.com/google/deepdream/blob/master/dream.ipynb), turning it into a Python module, while keeping in mind the importance of extensibility, such as custom step functions. 7 | 8 | If you're looking for a more advanced CNN visualization tool, check out Justin Johnson's [cnn-vis](https://github.com/jcjohnson/cnn-vis) library. 9 | 10 | ## Installation 11 | The `bat-country` packages requires [Caffe, an open-source CNN implementation from Berkeley](http://caffe.berkeleyvision.org/), to be already installed on your system. I detail the steps required to get Caffe up and running on your system in the [official bat-country release post](http://www.pyimagesearch.com/2015/07/06/bat-country-an-extendible-lightweight-python-package-for-deep-dreaming-with-caffe-and-convolutional-neural-networks/). An excellent alternative is to use the [Docker image](https://github.com/VISIONAI/clouddream) provided by Tomasz of Vision.ai. Using the Docker image will get you up and running quite painlessly. 12 | 13 | After you have Caffe setup and working, `bat-country` is a breeze to install. Just use pip: 14 | 15 | 
$ pip install bat-country
16 | 17 | You can also install `bat-country` by pulling down the repository and `setup.py`: 18 | 19 | 
$ git clone https://github.com/jrosebr1/bat-country.git
20 | $ pip install -r requirements.txt
21 | $ python setup.py install
22 | 23 | ## A simple example 24 | As I mentioned, one of the goals of `bat-country` is simplicity. Provided you have already installed Caffe and `bat-country` on your system, it only takes 3 lines of Python code to generate a deep dream/inceptionism image: 25 | 26 | 
# we can't stop here...
27 | bc = BatCountry("caffe/models/bvlc_googlenet")
28 | image = bc.dream(np.float32(Image.open("/path/to/image.jpg")))
29 | bc.cleanup()
30 | 31 | After executing this code, you can then take the `image` returned by the `dream` method and write it to file: 32 | 33 | 
result = Image.fromarray(np.uint8(image))
34 | result.save("/path/to/output.jpg")
35 | 36 | And that's it! 37 | 38 | For more information on `bat-country`, along with more code examples, head over to the the official announcement post on the PyImageSearch blog: 39 | 40 | [http://www.pyimagesearch.com/2015/07/06/bat-country-an-extendible-lightweight-python-package-for-deep-dreaming-with-caffe-and-convolutional-neural-networks/](http://www.pyimagesearch.com/2015/07/06/bat-country-an-extendible-lightweight-python-package-for-deep-dreaming-with-caffe-and-convolutional-neural-networks/) 41 | 42 | ## Guided dreaming 43 | Google has also demonstrated that it's possible to *guide* your dreaming process by supplying a *seed image*. This method passes your input image through the network in a similar manner, but this time using your seed image to guide the output. 44 | 45 | Using `bat-country`, it's just as easy to perform *guided dreaming* as *deep dreaming*. Here's some quick sample code: 46 | 47 | 
bc = BatCountry(args.base_model)
48 | features = bc.prepare_guide(Image.open(args.guide_image), end=args.layer)
49 | image = bc.dream(np.float32(Image.open(args.image)), end=args.layer,
50 | 	iter_n=20, objective_fn=BatCountry.guided_objective,
51 | 	objective_features=features,)
52 | bc.cleanup()
53 | 54 | What's nice about this approach is that I "guide" what the output image looks like. Here I use a seed image of Vincent van Gogh's Starry Night and apply to an image of clouds: 55 | 56 | 57 | 58 | As you can see, the output cloud image after applying guided dreaming appears to mimic many of the brush strokes of Van Gogh's painting: 59 | 60 | 61 | 62 | Here's another example, this time using a seed image of an antelope: 63 | 64 | 65 | 66 | You can read more about guided dreaming, plus view more example images here: 67 | 68 | [http://www.pyimagesearch.com/2015/07/13/generating-art-with-guided-deep-dreaming/](http://www.pyimagesearch.com/2015/07/13/generating-art-with-guided-deep-dreaming/) 69 | 70 | ## Some visual examples 71 | Below are a few example images generated using the `bat-country` package: 72 | 73 |
74 |

75 | 76 |
77 |

78 | 79 |
80 |

81 | 82 |
83 |

84 | 85 |
86 |

-------------------------------------------------------------------------------- /batcountry/__init__.py: -------------------------------------------------------------------------------- 1 | __author__ = 'adrianrosebrock' 2 | 3 | # import the necessary packages 4 | from batcountry import BatCountry -------------------------------------------------------------------------------- /batcountry/batcountry.py: -------------------------------------------------------------------------------- 1 | # import the necessary packages 2 | from __future__ import print_function 3 | from google.protobuf import text_format 4 | from cStringIO import StringIO 5 | from PIL import Image 6 | import scipy.ndimage as nd 7 | import numpy as np 8 | import caffe 9 | import os 10 | 11 | class BatCountry: 12 | def __init__(self, base_path, deploy_path=None, model_path=None, 13 | patch_model="./tmp.prototxt", mean=(104.0, 116.0, 122.0), 14 | channels=(2, 1, 0)): 15 | # if the deploy path is None, set the default 16 | if deploy_path is None: 17 | deploy_path = base_path + "/deploy.prototxt" 18 | 19 | # if the model path is None, set it to the default GoogleLeNet model 20 | if model_path is None: 21 | model_path = base_path + "/bvlc_googlenet.caffemodel" 22 | 23 | # check to see if the model should be patched to compute gradients 24 | if patch_model: 25 | model = caffe.io.caffe_pb2.NetParameter() 26 | text_format.Merge(open(deploy_path).read(), model) 27 | model.force_backward = True 28 | f = open(patch_model, "w") 29 | f.write(str(model)) 30 | f.close() 31 | 32 | # load the network and store the patched model path 33 | self.net = caffe.Classifier(patch_model, model_path, mean=np.float32(mean), 34 | channel_swap=channels) 35 | self.patch_model = patch_model 36 | 37 | def dream(self, image, iter_n=10, octave_n=4, octave_scale=1.4, 38 | end="inception_4c/output", clip=True, step_fn=None, objective_fn=None, 39 | preprocess_fn=None, deprocess_fn=None, verbose=True, visualize=False, 40 | **step_params): 41 | # if a step function has not been supplied, initialize it as the 42 | # standard gradient ascent step 43 | if step_fn is None: 44 | step_fn = BatCountry.gradient_ascent_step 45 | 46 | # if the objective function has not been supplied, initialize it 47 | # as the L2 objective 48 | if objective_fn is None: 49 | objective_fn = BatCountry.L2_objective 50 | 51 | # if the preprocess function has not been supplied, initialize it 52 | if preprocess_fn is None: 53 | preprocess_fn = BatCountry.preprocess 54 | 55 | # if the deprocess function has not been supplied, initialize it 56 | if deprocess_fn is None: 57 | deprocess_fn = BatCountry.deprocess 58 | 59 | # initialize the visualization list 60 | visualizations = [] 61 | 62 | # prepare base images for all octaves 63 | octaves = [preprocess_fn(self.net, image)] 64 | 65 | for i in xrange(octave_n - 1): 66 | octaves.append(nd.zoom(octaves[-1], (1, 1.0 / octave_scale, 67 | 1.0 / octave_scale), order=1)) 68 | 69 | # allocate image for network-produced details 70 | detail = np.zeros_like(octaves[-1]) 71 | src = self.net.blobs["data"] 72 | 73 | for octave, octave_base in enumerate(octaves[::-1]): 74 | h, w = octave_base.shape[-2:] 75 | 76 | if octave > 0: 77 | # upscale details from the previous octave 78 | h1, w1 = detail.shape[-2:] 79 | detail = nd.zoom(detail, (1, 1.0 * h/ h1, 1.0 * w / w1), order=1) 80 | 81 | # resize the network's input image size 82 | src.reshape(1, 3, h, w) 83 | src.data[0] = octave_base + detail 84 | 85 | for i in xrange(iter_n): 86 | step_fn(self.net, end=end, clip=clip, objective_fn=objective_fn, 87 | **step_params) 88 | 89 | # visualization 90 | vis = deprocess_fn(self.net, src.data[0]) 91 | 92 | # adjust image contrast if clipping is disabled 93 | if not clip: 94 | vis = vis * (255.0 / np.percentile(vis, 99.98)) 95 | 96 | if verbose: 97 | print("octave={}, iter={}, layer={}, image_dim={}".format(octave, 98 | i, end, vis.shape)) 99 | 100 | # check to see if the visualization list should be 101 | # updated 102 | if visualize: 103 | k = "octave_{}-iter_{}-layer_{}".format(octave, i, 104 | end.replace("/", "_")) 105 | visualizations.append((k, vis)) 106 | 107 | # extract details produced on the current octave 108 | detail = src.data[0] - octave_base 109 | 110 | # grab the resulting image 111 | r = deprocess_fn(self.net, src.data[0]) 112 | 113 | # check to see if the visualizations should be included 114 | if visualize: 115 | r = (r, visualizations) 116 | 117 | return r 118 | 119 | @staticmethod 120 | def gradient_ascent_step(net, step_size=1.5, end="inception_4c/output", 121 | jitter=32, clip=True, objective_fn=None, **objective_params): 122 | # if the objective function is None, initialize it as 123 | # the standard L2 objective 124 | if objective_fn is None: 125 | objective_fn = BatCountry.L2_objective 126 | 127 | # input image is stored in Net's 'data' blob 128 | src = net.blobs["data"] 129 | dst = net.blobs[end] 130 | 131 | # apply jitter shift 132 | ox, oy = np.random.randint(-jitter, jitter + 1, 2) 133 | src.data[0] = np.roll(np.roll(src.data[0], ox, -1), oy, -2) 134 | 135 | net.forward(end=end) 136 | objective_fn(dst, **objective_params) 137 | net.backward(start=end) 138 | g = src.diff[0] 139 | 140 | # apply normalized ascent step to the input image 141 | src.data[:] += step_size / np.abs(g).mean() * g 142 | 143 | # unshift image 144 | src.data[0] = np.roll(np.roll(src.data[0], -ox, -1), -oy, -2) 145 | 146 | # unshift image 147 | if clip: 148 | bias = net.transformer.mean["data"] 149 | src.data[:] = np.clip(src.data, -bias, 255 - bias) 150 | 151 | def layers(self): 152 | # return the layers of the network 153 | return self.net._layer_names 154 | 155 | def cleanup(self): 156 | # remove the patched model from disk 157 | os.remove(self.patch_model) 158 | 159 | def prepare_guide(self, image, end="inception_4c/output", maxW=224, maxH=224, 160 | preprocess_fn=None): 161 | # if the preprocess function has not been supplied, initialize it 162 | if preprocess_fn is None: 163 | preprocess_fn = BatCountry.preprocess 164 | 165 | # grab dimensions of input image 166 | (w, h) = image.size 167 | 168 | # GoogLeNet was trained on images with maximum width and heights 169 | # of 224 pixels -- if either dimension is larger than 224 pixels, 170 | # then we'll need to do some resizing 171 | nW, nH = 244, 244 172 | if w != 244 or h != 244: 173 | image = np.float32(image.resize((nW, nH), Image.BILINEAR)) 174 | 175 | (src, dst) = (self.net.blobs["data"], self.net.blobs[end]) 176 | src.reshape(1, 3, nH, nW) 177 | src.data[0] = preprocess_fn(self.net, image) 178 | self.net.forward(end=end) 179 | guide_features = dst.data[0].copy() 180 | 181 | return guide_features 182 | 183 | @staticmethod 184 | def L2_objective(dst): 185 | dst.diff[:] = dst.data 186 | 187 | @staticmethod 188 | def guided_objective(dst, objective_features): 189 | x = dst.data[0].copy() 190 | y = objective_features 191 | ch = x.shape[0] 192 | x = x.reshape(ch,-1) 193 | y = y.reshape(ch,-1) 194 | 195 | # compute the matrix of dot-products with guide features 196 | A = x.T.dot(y) 197 | 198 | # select ones that match best 199 | dst.diff[0].reshape(ch, -1)[:] = y[:,A.argmax(1)] 200 | 201 | @staticmethod 202 | def preprocess(net, img): 203 | return np.float32(np.rollaxis(img, 2)[::-1]) - net.transformer.mean["data"] 204 | 205 | @staticmethod 206 | def deprocess(net, img): 207 | return np.dstack((img + net.transformer.mean["data"])[::-1]) 208 | -------------------------------------------------------------------------------- /demo.py: -------------------------------------------------------------------------------- 1 | # USAGE 2 | # python demo.py --base-model $CAFFE_ROOT/models/bvlc_googlenet \ 3 | # --image initial_images/fear_and_loathing/fal_01.jpg \ 4 | # --output examples/simple_fal.jpg 5 | 6 | # import the necessary packages 7 | from batcountry import BatCountry 8 | from PIL import Image 9 | import numpy as np 10 | import argparse 11 | 12 | # construct the argument parser and parse the arguments 13 | ap = argparse.ArgumentParser() 14 | ap.add_argument("-b", "--base-model", required=True, help="base model path") 15 | ap.add_argument("-l", "--layer", type=str, default="conv2/3x3", 16 | help="layer of CNN to use") 17 | ap.add_argument("-i", "--image", required=True, help="path to base image") 18 | ap.add_argument("-o", "--output", required=True, help="path to output image") 19 | args = ap.parse_args() 20 | 21 | # we can't stop here... 22 | bc = BatCountry(args.base_model) 23 | image = bc.dream(np.float32(Image.open(args.image)), end=args.layer) 24 | bc.cleanup() 25 | 26 | # write the output image to file 27 | result = Image.fromarray(np.uint8(image)) 28 | result.save(args.output) -------------------------------------------------------------------------------- /demo_bulk.py: -------------------------------------------------------------------------------- 1 | # USAGE 2 | # python demo_bulk.py --base-model $CAFFE_ROOT/models/bvlc_googlenet \ 3 | # --images initial_images/the_matrix --output examples/output/the_matrix 4 | 5 | # import the necessary packages 6 | from __future__ import print_function 7 | from batcountry import BatCountry 8 | from imutils import paths 9 | from PIL import Image 10 | import numpy as np 11 | import argparse 12 | 13 | # construct the argument parser and parse the arguments 14 | ap = argparse.ArgumentParser() 15 | ap.add_argument("-b", "--base-model", required=True, 16 | help="base model path") 17 | ap.add_argument("-i", "--images", required=True, 18 | help="base path to input directory of images") 19 | ap.add_argument("-o", "--output", required=True, 20 | help="base path to output directory") 21 | args = ap.parse_args() 22 | 23 | # buy the ticket, take the ride 24 | bc = BatCountry(args.base_model) 25 | layers = ("conv2/3x3", "inception_3b/5x5_reduce", "inception_4c/output") 26 | 27 | # loop over the input directory of images 28 | for imagePath in paths.list_images(args.images): 29 | # loop over the layers 30 | for layer in layers: 31 | # we can't stop here... 32 | print("[INFO] processing `{}`".format(imagePath)) 33 | image = bc.dream(np.float32(Image.open(imagePath)), end=layer) 34 | 35 | # write the output image to file 36 | filename = imagePath[imagePath.rfind("/") + 1:] 37 | outputPath = "{}/{}_{}".format(args.output, layer.replace("/", "_"), filename) 38 | result = Image.fromarray(np.uint8(image)) 39 | result.save(outputPath) 40 | 41 | # do some cleanup 42 | bc.cleanup() 43 | -------------------------------------------------------------------------------- /demo_guided.py: -------------------------------------------------------------------------------- 1 | # USAGE 2 | # python demo_guided.py --base-model $CAFFE_ROOT/models/bvlc_googlenet \ 3 | # --image initial_images/clouds.jpg \ 4 | # --guide-image initial_images/seed_images/starry_night.jpg \ 5 | # --output examples/output/seeded/clouds_and_starry_night.jpg 6 | 7 | # import the necessary packages 8 | from batcountry import BatCountry 9 | from PIL import Image 10 | import numpy as np 11 | import argparse 12 | 13 | # construct the argument parser and parse the arguments 14 | ap = argparse.ArgumentParser() 15 | ap.add_argument("-b", "--base-model", required=True, help="base model path") 16 | ap.add_argument("-l", "--layer", type=str, default="inception_4c/output", 17 | help="layer of CNN to use") 18 | ap.add_argument("-i", "--image", required=True, help="path to base image") 19 | ap.add_argument("-g", "--guide-image", required=True, help="path to guide image") 20 | ap.add_argument("-o", "--output", required=True, help="path to output image") 21 | args = ap.parse_args() 22 | 23 | # we can't stop here... 24 | bc = BatCountry(args.base_model) 25 | features = bc.prepare_guide(Image.open(args.guide_image), end=args.layer) 26 | image = bc.dream(np.float32(Image.open(args.image)), end=args.layer, 27 | iter_n=20, objective_fn=BatCountry.guided_objective, 28 | objective_features=features,) 29 | bc.cleanup() 30 | 31 | # write the output image to file 32 | result = Image.fromarray(np.uint8(image)) 33 | result.save(args.output) -------------------------------------------------------------------------------- /demo_vis.py: -------------------------------------------------------------------------------- 1 | # USAGE 2 | # python demo_vis.py --base-model $CAFFE_ROOT/models/bvlc_googlenet \ 3 | # --image initial_images/fear_and_loathing/fal_01.jpg \ 4 | # --vis examples/output/visualizations 5 | 6 | # import the necessary packages 7 | from batcountry import BatCountry 8 | from PIL import Image 9 | import numpy as np 10 | import argparse 11 | 12 | # construct the argument parser and parse the arguments 13 | ap = argparse.ArgumentParser() 14 | ap.add_argument("-b", "--base-model", required=True, help="base model path") 15 | ap.add_argument("-l", "--layer", type=str, default="conv2/3x3", 16 | help="layer of CNN to use") 17 | ap.add_argument("-i", "--image", required=True, help="path to base image") 18 | ap.add_argument("-v", "--vis", required=True, 19 | help="path to output directory for visualizations") 20 | args = ap.parse_args() 21 | 22 | # we can't stop here... 23 | bc = BatCountry(args.base_model) 24 | (image, visualizations) = bc.dream(np.float32(Image.open(args.image)), 25 | end=args.layer, visualize=True) 26 | bc.cleanup() 27 | 28 | # loop over the visualizations 29 | for (k, vis) in visualizations: 30 | # write the visualization to file 31 | outputPath = "{}/{}.jpg".format(args.vis, k) 32 | result = Image.fromarray(np.uint8(vis)) 33 | result.save(outputPath) -------------------------------------------------------------------------------- /docs/images/clouds_and_antelope_example.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jrosebr1/bat-country/894af13bd777ab8aa989baf520db074b97cfad9a/docs/images/clouds_and_antelope_example.jpg 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Pillow==2.9.0 2 | argparse==1.2.1 3 | decorator==3.4.2 4 | imutils==0.2.2 5 | matplotlib==1.4.3 6 | mock==1.0.1 7 | networkx==1.9.1 8 | nose==1.3.7 9 | numpy==1.9.2 10 | protobuf==2.6.1 11 | pyparsing==2.0.3 12 | python-dateutil==2.4.2 13 | pytz==2015.4 14 | scikit-image==0.11.3 15 | scipy==0.15.1 16 | six==1.9.0 17 | wsgiref==0.1.2 18 | -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | [metadata] 2 | description-file = README.md -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from distutils.core import setup 2 | 3 | setup( 4 | name='bat-country', 5 | packages=['batcountry'], 6 | version='0.2', 7 | description='A lightweight, extendible, easy to use Python package for deep dreaming and image generation with Caffe and CNNs', 8 | author='Adrian Rosebrock', 9 | author_email='adrian@pyimagesearch.com', 10 | url='https://github.com/jrosebr1/bat-country', 11 | download_url='https://github.com/jrosebr1/bat-country/tarball/0.1', 12 | license='MIT', 13 | install_requires=[ 14 | 'Pillow==2.9.0', 15 | 'argparse==1.2.1', 16 | 'decorator==3.4.2', 17 | 'imutils==0.2.2', 18 | 'matplotlib==1.4.3', 19 | 'mock==1.0.1', 20 | 'networkx==1.9.1', 21 | 'nose==1.3.7', 22 | 'numpy==1.9.2', 23 | 'protobuf==2.6.1', 24 | 'pyparsing==2.0.3', 25 | 'python-dateutil==2.4.2', 26 | 'pytz==2015.4', 27 | 'scikit-image==0.11.3', 28 | 'scipy==0.15.1', 29 | 'six==1.9.0', 30 | 'wsgiref==0.1.2', 31 | ], 32 | keywords=['computer vision', 'machine learning', 'deep learning', 33 | 'convolutional neural network', 'deep dream', 'inceptionism'], 34 | classifiers=[], 35 | ) 36 | --------------------------------------------------------------------------------