illustration2vec (i2v) is a simple library for estimating a set of tags and
extracting semantic feature vectors from given illustrations.
For details, please see
our main paper.
- Pre-trained models (
i2vuses Convolutional Neural Networks. Please download several pre-trained models from here, or executeget_models.shin this repository). numpyandscipyPIL(Python Imaging Library) or its alternatives (e.g.,Pillow)skimage(Image processing library for python)
In addition to the above libraries and the pre-trained models, i2v requires
either caffe or chainer library. If you are not familiar with deep
learning libraries, we recommend to use chainer that can be installed
via pip command.
In this section, we show two simple examples -- tag prediction and the the feature vector extraction -- by using the following illustration [1].
[1] Hatsune Miku (初音ミク), © Crypton Future Media, INC., http://piapro.net/en_for_creators.html. This image is licensed under the Creative Commons - Attribution-NonCommercial, 3.0 Unported (CC BY-NC).
i2v estimates a number of semantic tags from given illustrations
in the following manner.
import i2v
from PIL import Image
illust2vec = i2v.make_i2v_with_chainer(
"illust2vec_tag_ver200.caffemodel", "tag_list.json")
# In the case of caffe, please use i2v.make_i2v_with_caffe instead:
# illust2vec = i2v.make_i2v_with_caffe(
# "illust2vec_tag.prototxt", "illust2vec_tag_ver200.caffemodel",
# "tag_list.json")
img = Image.open("images/miku.jpg")
illust2vec.estimate_plausible_tags([img], threshold=0.5)estimate_plausible_tags() returns dictionaries that have a pair of
tag and its confidence.
[{'character': [(u'hatsune miku', 0.9999994039535522)],
'copyright': [(u'vocaloid', 0.9999998807907104)],
'general': [(u'thighhighs', 0.9956372380256653),
(u'1girl', 0.9873462319374084),
(u'twintails', 0.9812833666801453),
(u'solo', 0.9632901549339294),
(u'aqua hair', 0.9167950749397278),
(u'long hair', 0.8817108273506165),
(u'very long hair', 0.8326570987701416),
(u'detached sleeves', 0.7448858618736267),
(u'skirt', 0.6780789494514465),
(u'necktie', 0.5608364939689636),
(u'aqua eyes', 0.5527772307395935)],
'rating': [(u'safe', 0.9785731434822083),
(u'questionable', 0.020535090938210487),
(u'explicit', 0.0006299660308286548)]}]These tags are classified into the following four categories: general tags representing general attributes included in an image, copyright tags representing the specific name of the copyright, character tags representing the specific name of the characters, and rating tags representing X ratings.
If you want to focus on several specific tags, use estimate_specific_tags() instead.
illust2vec.estimate_specific_tags([img], ["1girl", "blue eyes", "safe"])
# -> [{'1girl': 0.9873462319374084, 'blue eyes': 0.01301183458417654, 'safe': 0.9785731434822083}]i2v can extract a semantic feature vector from an illustration.
import i2v
from PIL import Image
# In the feature vector extraction, you do not need to specify the tag.
illust2vec = i2v.make_i2v_with_chainer("illust2vec_ver200.caffemodel")
# illust2vec = i2v.make_i2v_with_caffe(
# "illust2vec.prototxt", "illust2vec_ver200.caffemodel")
img = Image.open("images/miku.jpg")
# extract a 4,096-dimensional feature vector
result_real = illust2vec.extract_feature([img])
print("shape: {}, dtype: {}".format(result_real.shape, result_real.dtype))
print(result_real)
# i2v also supports a 4,096-bit binary feature vector
result_binary = illust2vec.extract_binary_feature([img])
print("shape: {}, dtype: {}".format(result_binary.shape, result_binary.dtype))
print(result_binary)The output is the following:
shape: (1, 4096), dtype: float32
[[ 7.47459459 3.68610668 0.5379501 ..., -0.14564702 2.71820974
7.31408596]]
shape: (1, 512), dtype: uint8
[[246 215 87 107 249 190 101 32 187 18 124 90 57 233 245 243 245 54
229 47 188 147 161 149 149 232 59 217 117 112 243 78 78 39 71 45
235 53 49 77 49 211 93 136 235 22 150 195 131 172 141 253 220 104
163 220 110 30 59 182 252 253 70 178 148 152 119 239 167 226 202 58
179 198 67 117 226 13 204 246 215 163 45 150 158 21 244 214 245 251
124 155 86 250 183 96 182 90 199 56 31 111 123 123 190 79 247 99
89 233 61 105 58 13 215 159 198 92 121 39 170 223 79 245 83 143
175 229 119 127 194 217 207 242 27 251 226 38 204 217 125 175 215 165
251 197 234 94 221 188 147 247 143 247 124 230 239 34 47 195 36 39
111 244 43 166 118 15 81 177 7 56 132 50 239 134 78 207 232 188
194 122 169 215 124 152 187 150 14 45 245 27 198 120 146 108 120 250
199 178 22 86 175 102 6 237 111 254 214 107 219 37 102 104 255 226
206 172 75 109 239 189 211 48 105 62 199 238 211 254 255 228 178 189
116 86 135 224 6 253 98 54 252 168 62 23 163 177 255 58 84 173
156 84 95 205 140 33 176 150 210 231 221 32 43 201 73 126 4 127
190 123 115 154 223 79 229 123 241 154 94 250 8 236 76 175 253 247
240 191 120 174 116 229 37 117 222 214 232 175 255 176 154 207 135 183
158 136 189 84 155 20 64 76 201 28 109 79 141 188 21 222 71 197
228 155 94 47 137 250 91 195 201 235 249 255 176 245 112 228 207 229
111 232 157 6 216 228 55 153 202 249 164 76 65 184 191 188 175 83
231 174 158 45 128 61 246 191 210 189 120 110 198 126 98 227 94 127
104 214 77 237 91 235 249 11 246 247 30 152 19 118 142 223 9 245
196 249 255 0 113 2 115 149 196 59 157 117 252 190 120 93 213 77
222 215 43 223 222 106 138 251 68 213 163 57 54 252 177 250 172 27
92 115 104 231 54 240 231 74 60 247 23 242 238 176 136 188 23 165
118 10 197 183 89 199 220 95 231 61 214 49 19 85 93 41 199 21
254 28 205 181 118 153 170 155 187 60 90 148 189 218 187 172 95 182
250 255 147 137 157 225 127 127 42 55 191 114 45 238 228 222 53 94
42 181 38 254 177 232 150 99]]
The pre-trained models and the other files we have provided are licensed under the MIT License.
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