├── requirements.txt ├── .gitignore ├── neural_art ├── models │ ├── __init__.py │ ├── nin.py │ └── vgg.py ├── __init__.py ├── image_converters │ ├── __init__.py │ ├── large_image_converter.py │ ├── multi_reference_image_converter.py │ └── image_converter.py └── utility.py ├── bin ├── convert_image.py ├── convert_large_image.py ├── convert_image_multistyle.py ├── convert_video.py └── convert_image_multi.py ├── README.md └── LICENSE /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | openopt 3 | cvxopt 4 | chainer 5 | pillow 6 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.caffemodel 2 | .idea/ 3 | *.png 4 | *.jpg 5 | *.dump 6 | *.pyc -------------------------------------------------------------------------------- /neural_art/models/__init__.py: -------------------------------------------------------------------------------- 1 | from .vgg import * 2 | from .nin import * 3 | -------------------------------------------------------------------------------- /neural_art/__init__.py: -------------------------------------------------------------------------------- 1 | from . import utility 2 | from . import models 3 | from . import image_converters 4 | -------------------------------------------------------------------------------- /neural_art/image_converters/__init__.py: -------------------------------------------------------------------------------- 1 | from .image_converter import * 2 | from .multi_reference_image_converter import * 3 | from .large_image_converter import * 4 | -------------------------------------------------------------------------------- /bin/convert_image.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import sys 3 | import os 4 | 5 | sys.path.append(os.path.join(os.path.dirname(__file__), "..")) 6 | import neural_art 7 | 8 | parser = argparse.ArgumentParser() 9 | parser.add_argument("content_image") 10 | parser.add_argument("texture_image") 11 | parser.add_argument("feature_type") 12 | parser.add_argument("--content_weight", type=float, default=0.005) 13 | parser.add_argument("--gpu", type=int, default=-1) 14 | args = parser.parse_args() 15 | 16 | texture_img = neural_art.utility.load_image(args.texture_image) 17 | texture_img = neural_art.utility.resize_img(texture_img, 300) 18 | 19 | if args.feature_type == "matrix": 20 | converter = neural_art.image_converters.ImageConverterMatrix(texture_img, gpu=args.gpu, content_weight=args.content_weight, texture_weight=1) 21 | if args.feature_type == "vector": 22 | converter = neural_art.image_converters.ImageConverter(texture_img, gpu=args.gpu, content_weight=args.content_weight, texture_weight=1) 23 | 24 | content_img = neural_art.utility.load_image(args.content_image) 25 | content_img = neural_art.utility.resize_img(content_img, 300) 26 | converter.convert(content_img, content_img, iteration=100).save("converted2.png") 27 | -------------------------------------------------------------------------------- /neural_art/models/nin.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import print_function 3 | import chainer 4 | import chainer.functions 5 | import chainer.links.caffe 6 | 7 | 8 | class NIN(object): 9 | def __init__(self, caffemodelpath, alpha=[0, 0, 1, 1], 10 | beta=[0.000244140625, 6.103515625e-05, 1.52587890625e-05, 3.814697265625e-06], model=None): 11 | if model is None: 12 | self.model = chainer.links.caffe.CaffeFunction(caffemodelpath) 13 | else: 14 | self.model = model 15 | self.alpha = alpha 16 | self.beta = beta 17 | 18 | def forward_layers(self, x, average_pooling=False): 19 | if average_pooling: 20 | pooling = lambda x: chainer.functions.average_pooling_2d(chainer.functions.relu(x), 3, stride=2) 21 | else: 22 | pooling = lambda x: chainer.functions.max_pooling_2d(chainer.functions.relu(x), 3, stride=2) 23 | y0 = chainer.functions.relu(self.model.conv1(x)) 24 | y1 = self.model.cccp2(chainer.functions.relu(self.model.cccp1(y0))) 25 | x1 = chainer.functions.relu(self.model.conv2(pooling(chainer.functions.relu(y1)))) 26 | y2 = self.model.cccp4(chainer.functions.relu(self.model.cccp3(x1))) 27 | x2 = chainer.functions.relu(self.model.conv3(pooling(chainer.functions.relu(y2)))) 28 | y3 = self.model.cccp6(chainer.functions.relu(self.model.cccp5(x2))) 29 | x3 = chainer.functions.relu(getattr(self.model, "conv4-1024")( 30 | chainer.functions.dropout(pooling(chainer.functions.relu(y3))))) 31 | return [y0, x1, x2, x3] 32 | -------------------------------------------------------------------------------- /neural_art/models/vgg.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import chainer 3 | import chainer.functions 4 | import chainer.links.caffe 5 | 6 | 7 | class VGG(object): 8 | def __init__(self, caffemodelpath, alpha=[0, 0, 1, 1], 9 | beta=[0.000244140625, 6.103515625e-05, 1.52587890625e-05, 3.814697265625e-06], no_padding=False, 10 | model=None): ### beta is decided by experiments 11 | if model is None: 12 | self.model = chainer.links.caffe.CaffeFunction(caffemodelpath) 13 | else: 14 | self.model = model 15 | self.alpha = alpha 16 | self.beta = beta 17 | if no_padding: 18 | for layer in self.model.children(): 19 | if not layer.name.find("conv") == -1: 20 | layer.pad = 0 21 | 22 | def forward_layers(self, x, average_pooling=False): 23 | if average_pooling: 24 | pooling = lambda x: chainer.functions.average_pooling_2d(chainer.functions.relu(x), 2, stride=2) 25 | else: 26 | pooling = lambda x: chainer.functions.max_pooling_2d(chainer.functions.relu(x), 2, stride=2) 27 | 28 | y1 = self.model.conv1_2(chainer.functions.relu(self.model.conv1_1(x))) 29 | x1 = pooling(y1) 30 | 31 | y2 = self.model.conv2_2(chainer.functions.relu(self.model.conv2_1(x1))) 32 | x2 = pooling(y2) 33 | 34 | y3 = self.model.conv3_3( 35 | chainer.functions.relu(self.model.conv3_2(chainer.functions.relu(self.model.conv3_1(x2))))) 36 | x3 = pooling(y3) 37 | 38 | y4 = self.model.conv4_3( 39 | chainer.functions.relu(self.model.conv4_2(chainer.functions.relu(self.model.conv4_1(x3))))) 40 | return [y1, y2, y3, y4] 41 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | neural_style_synthesizer 2 | ========================== 3 | 4 | INSTALL 5 | --------------- 6 | 7 | The model files of neural networks are not contained in this repository. 8 | You can get them from [nin_imagenet.caffemodel](https://gist.github.com/mavenlin/d802a5849de39225bcc6) and [VGG_ILSVRC_16_layers.caffemodel](https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md). 9 | 10 | Dependent libraries are installed with the following script. 11 | 12 | ``` 13 | pip install numpy 14 | pip install -r requirements.txt 15 | ``` 16 | 17 | RUN 18 | ---------------- 19 | 20 | ### Whole style transfer 21 | 22 | You can transfer whole patch from one to another. 23 | 24 | with CPU 25 | 26 | ``` 27 | python bin/convert_image_multi.py \ 28 | --modelpath=./VGG_ILSVRC_16_layers.caffemodel \ 29 | --iteration=100 \ 30 | --gpu=-1 \ 31 | --xsplit=1 --ysplit=1 --resize=300 \ 32 | input.png \ 33 | style.png \ 34 | --output_image=./converted.png 35 | ``` 36 | 37 | with GPU 38 | 39 | ``` 40 | python bin/convert_image_multi.py \ 41 | --modelpath=./VGG_ILSVRC_16_layers.caffemodel \ 42 | --iteration=100 \ 43 | --gpu=0 \ 44 | --xsplit=1 --ysplit=1 --resize=300 \ 45 | input.png \ 46 | style.png \ 47 | --output_image=./converted.png 48 | ``` 49 | 50 | ### Partial style transfer 51 | 52 | Choose optimal patches from style image and transfer them to another image. 53 | Split style image to 2x2 54 | 55 | ``` 56 | python bin/convert_image_multi.py \ 57 | --modelpath=./VGG_ILSVRC_16_layers.caffemodel \ 58 | --iteration=100 \ 59 | --gpu=0 \ 60 | --xsplit=2 --ysplit=2 --resize=300 \ 61 | --model=vgg_nopad\ 62 | input.png \ 63 | style.png \ 64 | --output_image=./converted_optimal_2x2.png 65 | ``` 66 | 67 | ### Style transferred video 68 | 69 | Tranfer style on video frame using last frame's result. 70 | 71 | ``` 72 | python bin/convert_video.py \ 73 | --iteration=100 --model=vgg \ 74 | video.mp4 \ 75 | style.png \ 76 | output_directory 77 | ``` 78 | 79 | Then you can find the style transferred video at `output_directory/out.avi` after 100 x frame times calculation. 80 | 81 | ### Optimal Blended Texture Transfer 82 | 83 | Please see https://nico-opendata.jp/en/casestudy/neural_style_synthesizer/index.html for technical details. 84 | 85 | ``` 86 | python bin/convert_image_multistyle.py \ 87 | --model=vgg_nopad \ 88 | --iteration=100 \ 89 | --gpu=3 --xsplit=1 --ysplit=1 --resize=200 \ 90 | /path/to/input/file \ 91 | /path/to/directory/contains/multiple/refarence/files \ 92 | --debug --out_dir=/path/of/output 93 | ``` 94 | -------------------------------------------------------------------------------- /neural_art/utility.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pickle 3 | import neural_art 4 | import numpy 5 | import chainer.functions 6 | import chainer.serializers 7 | from PIL import Image 8 | 9 | 10 | def load_image(img_file): 11 | """ 12 | :return: Image 13 | """ 14 | img = Image.open(img_file) 15 | if len(img.size) == 2: # gray scale 16 | img_rgb = Image.new("RGB", img.size) 17 | img_rgb.paste(img) 18 | img = img_rgb 19 | return img 20 | 21 | 22 | def resize_img(img, max_length): 23 | """ 24 | :return: Image 25 | """ 26 | orig_w, orig_h = img.size[0], img.size[1] 27 | if orig_w < orig_h: 28 | new_w = max_length * orig_w // orig_h 29 | new_h = max_length 30 | else: 31 | new_w = max_length 32 | new_h = max_length * orig_h // orig_w 33 | return img.resize((new_w, new_h)) 34 | 35 | 36 | def load_nn(modelname, modelpath=None): 37 | cachepath = "{}.dump".format(modelname) 38 | model = None 39 | if os.path.exists(cachepath): 40 | model = pickle.load(open(cachepath, "rb")) 41 | 42 | if modelname == 'vgg': 43 | if modelpath is None: modelpath = "VGG_ILSVRC_16_layers.caffemodel" 44 | nn = neural_art.models.VGG(modelpath, no_padding=False, model=model) 45 | elif modelname == 'vgg_nopad': 46 | if modelpath is None: modelpath = "VGG_ILSVRC_16_layers.caffemodel" 47 | nn = neural_art.models.VGG(modelpath, no_padding=True, model=model) 48 | elif modelname == 'nin': 49 | if modelpath is None: modelpath = "nin_imagenet.caffemodel" 50 | nn = neural_art.models.NIN(modelpath, model=model) 51 | else: 52 | print('invalid model name.') 53 | exit(1) 54 | 55 | if model is None: 56 | with open(cachepath, "wb+") as f: 57 | pickle.dump(nn.model, f, pickle.HIGHEST_PROTOCOL) 58 | return nn 59 | 60 | 61 | def img2array(img): 62 | data_subtracted = numpy.asarray(img)[:, :, :3].astype(numpy.float32) - 128 63 | data = data_subtracted.transpose(2, 0, 1)[::-1] 64 | return numpy.array([data]) 65 | 66 | 67 | def array2img(array): 68 | def clip(a): 69 | return 0 if a < 0 else (255 if a > 255 else a) 70 | 71 | data_added = array[0][::-1].transpose(1, 2, 0) + 128 72 | data = numpy.vectorize(clip)(data_added).astype(numpy.uint8) 73 | return Image.fromarray(data) 74 | 75 | 76 | def get_matrix(y): 77 | ch = y.data.shape[1] 78 | w = y.data.shape[2] 79 | h = y.data.shape[3] 80 | y_2d = chainer.functions.reshape(y, (ch, w * h)) 81 | texture_matrix = chainer.functions.matmul(y_2d, y_2d, transb=True) / numpy.float32(w * h) 82 | return texture_matrix 83 | 84 | 85 | def print_ltsv(raw_dict): 86 | items = [] 87 | for key, value in raw_dict.items(): 88 | items.append("{}:{}".format(key, value)) 89 | print("\t".join(items)) 90 | -------------------------------------------------------------------------------- /bin/convert_large_image.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import sys 3 | import os 4 | import chainer.optimizers 5 | 6 | sys.path.append(os.path.join(os.path.dirname(__file__), "..")) 7 | import neural_art 8 | 9 | 10 | parser = argparse.ArgumentParser() 11 | parser.add_argument("content_image") 12 | parser.add_argument("texture_image") 13 | parser.add_argument("--content_weight", type=float, default=0.005) 14 | parser.add_argument("--gpu", type=int, default=-1) 15 | parser.add_argument("--iteration", type=int, default=1000) 16 | parser.add_argument("--style_xsplit", type=int, default=1) 17 | parser.add_argument("--style_ysplit", type=int, default=1) 18 | parser.add_argument("--content_xsplit", type=int, default=3) 19 | parser.add_argument("--content_ysplit", type=int, default=3) 20 | parser.add_argument("--content_overwrap", type=int, default=100) 21 | parser.add_argument("--resize", type=int, default=300, 22 | help="maximum size of height and width for content and texture images") 23 | parser.add_argument("--out_dir", default="output") 24 | parser.add_argument("--no_optimize", dest="optimize", action="store_false") 25 | parser.add_argument("--output_image", default="converted.png") 26 | parser.add_argument("--debug", action="store_true") 27 | parser.add_argument("--debug_span", type=int, default=100) 28 | parser.add_argument("--average_pooling", action="store_true") 29 | parser.add_argument("--model", default="vgg") 30 | parser.add_argument("--random_init", action="store_true") 31 | parser.add_argument("--init_image", default=None) 32 | args = parser.parse_args() 33 | if args.init_image is None: args.init_image = args.content_image 34 | print(args) 35 | texture_img = neural_art.utility.load_image(args.texture_image) 36 | texture_img = neural_art.utility.resize_img(texture_img, args.resize) 37 | content_img = neural_art.utility.load_image(args.content_image) 38 | content_img = neural_art.utility.resize_img(content_img, args.resize) 39 | init_img = neural_art.utility.load_image(args.init_image) 40 | init_img = neural_art.utility.resize_img(init_img, args.resize) 41 | 42 | XSTEP = texture_img.size[0] / args.style_xsplit 43 | YSTEP = texture_img.size[1] / args.style_ysplit 44 | texture_imgs = [] 45 | for x_index in xrange(args.style_xsplit): 46 | x = x_index * XSTEP 47 | for y_index in xrange(args.style_ysplit): 48 | y = y_index * YSTEP 49 | texture_imgs.append(texture_img.crop([x, y, x+XSTEP, y+YSTEP])) 50 | 51 | model = neural_art.utility.load_nn(args.model) 52 | if not os.path.exists(args.out_dir): os.mkdir(args.out_dir) 53 | converter = neural_art.image_converters.LargeImageConverter( 54 | texture_imgs, model, gpu=args.gpu, optimizer=chainer.optimizers.Adam(alpha=4.0), 55 | content_weight=args.content_weight, texture_weight=1) 56 | converter.convert_debug( 57 | content_img, init_img=init_img, 58 | overwrap=args.content_overwrap, 59 | max_iteration=args.iteration, debug_span=args.debug_span, 60 | output_directory=args.out_dir, random_init=args.random_init, 61 | xsplit=args.content_xsplit, ysplit=args.content_ysplit).save(args.output_image) 62 | -------------------------------------------------------------------------------- /bin/convert_image_multistyle.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import sys 3 | import os 4 | 5 | sys.path.append(os.path.join(os.path.dirname(__file__), "..")) 6 | import neural_art 7 | 8 | parser = argparse.ArgumentParser() 9 | parser.add_argument("content_image") 10 | parser.add_argument("texture_image_dir") 11 | parser.add_argument("--content_weight", type=float, default=0.005) 12 | parser.add_argument("--texture_weight", type=float, default=1) 13 | parser.add_argument("--gpu", type=int, default=-1) 14 | parser.add_argument("--iteration", type=int, default=1000) 15 | parser.add_argument("--xsplit", type=int, default=1) 16 | parser.add_argument("--ysplit", type=int, default=1) 17 | parser.add_argument("--resize", type=int, default=300, 18 | help="maximum size of height and width for content and texture images") 19 | parser.add_argument("--out_dir", default="output") 20 | parser.add_argument("--no_optimize", dest="optimize", action="store_false") 21 | parser.add_argument("--output_image", default="converted.png") 22 | parser.add_argument("--debug", action="store_true") 23 | parser.add_argument("--debug_span", type=int, default=100) 24 | parser.add_argument("--average_pooling", action="store_true") 25 | parser.add_argument("--model", default="vgg") 26 | parser.add_argument("--random_init", action="store_true") 27 | parser.add_argument("--init_image", default=None) 28 | parser.add_argument("--only_layer", default=None, type=int) 29 | args = parser.parse_args() 30 | if args.init_image is None: args.init_image = args.content_image 31 | print(args) 32 | 33 | texture_imgs = [] 34 | for texture_image_filename in os.listdir(args.texture_image_dir): 35 | texture_image_filename = args.texture_image_dir + "/" + texture_image_filename 36 | texture_img = neural_art.utility.load_image(texture_image_filename) 37 | texture_img = neural_art.utility.resize_img(texture_img, args.resize) 38 | 39 | XSTEP = texture_img.size[0] / (args.xsplit) 40 | YSTEP = texture_img.size[1] / (args.ysplit) 41 | if XSTEP > 100 and YSTEP > 100: 42 | for x_index in xrange(args.xsplit): 43 | x = x_index * XSTEP 44 | for y_index in xrange(args.ysplit): 45 | y = y_index * YSTEP 46 | texture_imgs.append(texture_img.crop([x, y, x+XSTEP, y+YSTEP])) 47 | 48 | content_img = neural_art.utility.load_image(args.content_image) 49 | content_img = neural_art.utility.resize_img(content_img, args.resize) 50 | init_img = neural_art.utility.load_image(args.init_image) 51 | init_img = neural_art.utility.resize_img(init_img, args.resize) 52 | 53 | model = neural_art.utility.load_nn(args.model) 54 | if not args.only_layer is None: 55 | for i in range(len(model.beta)): 56 | if not (i == args.only_layer): 57 | model.beta[i] = 0 58 | converter = neural_art.image_converters.MultiReferenceImageConverter(texture_imgs, gpu=args.gpu, content_weight=args.content_weight, texture_weight=args.texture_weight, model=model, average_pooling=args.average_pooling) 59 | 60 | if args.debug: 61 | if not os.path.exists(args.out_dir): os.mkdir(args.out_dir) 62 | debug_span = args.debug_span 63 | else: 64 | debug_span = args.iteration * 2 65 | converter.convert_debug(content_img, init_img=init_img, 66 | max_iteration=args.iteration, debug_span=debug_span, output_directory=args.out_dir, 67 | optimize=args.optimize, random_init=args.random_init).save(args.output_image) 68 | -------------------------------------------------------------------------------- /bin/convert_video.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import print_function 3 | 4 | import cv2 5 | import PIL.Image 6 | import argparse 7 | import os 8 | import sys 9 | import numpy 10 | 11 | sys.path.append(os.path.join(os.path.dirname(__file__), "..")) 12 | import neural_art 13 | 14 | class VideoConverter(object): 15 | def __init__(self, frame_converter, iteration): 16 | """ 17 | :type frame_converter: neural_art.image_converters.BaseImageConverter 18 | """ 19 | self.frame_converter = frame_converter 20 | self.iteration = iteration 21 | 22 | def convert_video(self, video_path, output_directory, skip=0, resize=400): 23 | video = cv2.VideoCapture(video_path) 24 | video_output = None 25 | i = 0 26 | img_init = None 27 | while video.get(cv2.cv.CV_CAP_PROP_POS_AVI_RATIO) < 1.0: 28 | i += 1 29 | for _ in range(skip+1): 30 | status, bgr_img = video.read() 31 | img = PIL.Image.fromarray(cv2.cvtColor( 32 | bgr_img, 33 | cv2.COLOR_BGR2RGB 34 | )) 35 | img = neural_art.utility.resize_img(img, resize) 36 | if video_output is None: 37 | video_output = cv2.VideoWriter( 38 | "{}/out.avi".format(output_directory), 39 | fourcc=0, #raw 40 | fps=video.get(cv2.cv.CV_CAP_PROP_FPS) / (skip + 1), 41 | frameSize=img.size, 42 | isColor=True 43 | ) 44 | if(not video_output.isOpened()): 45 | raise(Exception("Cannot Open VideoWriter")) 46 | if img_init is None: 47 | img_init = img 48 | converted_img = self.frame_converter.convert(img, init_img=img_init, iteration=self.iteration) 49 | converted_img.save("{}/converted_{:05d}.png".format(output_directory, i)) 50 | img_init = converted_img 51 | video_output.write(cv2.cvtColor( 52 | numpy.asarray(converted_img), 53 | cv2.COLOR_RGB2BGR 54 | )) 55 | video_output.release() 56 | 57 | 58 | parser = argparse.ArgumentParser() 59 | parser.add_argument("video") 60 | parser.add_argument("texture_image") 61 | parser.add_argument("output_directory") 62 | parser.add_argument("--gpu", type=int, default=-1) 63 | parser.add_argument("--model", default="vgg") 64 | parser.add_argument("--content_weight", type=float, default=0.005) 65 | parser.add_argument("--texture_weight", type=float, default=1) 66 | parser.add_argument("--iteration", type=int, default=1000) 67 | parser.add_argument("--resize", type=int, default=400) 68 | args = parser.parse_args() 69 | print("arguments") 70 | print(args) 71 | 72 | try: 73 | os.mkdir(args.output_directory) 74 | except: 75 | pass 76 | model = neural_art.utility.load_nn(args.model) 77 | texture_img = neural_art.utility.load_image(args.texture_image) 78 | texture_img = neural_art.utility.resize_img(texture_img, args.resize) 79 | frame_converter = neural_art.image_converters.MultiReferenceImageConverter( 80 | texture_imgs=[texture_img], gpu=args.gpu, model=model, 81 | content_weight=args.content_weight, texture_weight=args.texture_weight, average_pooling=True) 82 | converter = VideoConverter(frame_converter, iteration=args.iteration) 83 | converter.convert_video(args.video, args.output_directory, resize=args.resize) 84 | -------------------------------------------------------------------------------- /bin/convert_image_multi.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import sys 3 | import os 4 | from builtins import range 5 | 6 | sys.path.append(os.path.join(os.path.dirname(__file__), "..")) 7 | import neural_art 8 | 9 | parser = argparse.ArgumentParser() 10 | parser.add_argument("content_image") 11 | parser.add_argument("texture_image") 12 | parser.add_argument("--content_weight", type=float, default=0.005) 13 | parser.add_argument("--gpu", type=int, default=-1) 14 | parser.add_argument("--iteration", type=int, default=1000) 15 | parser.add_argument("--xsplit", type=int, default=1) 16 | parser.add_argument("--ysplit", type=int, default=1) 17 | parser.add_argument("--resize", type=int, default=300, 18 | help="[depricated] maximum size of height and width for content and texture images") 19 | parser.add_argument("--resize_texture", type=int, default=None, 20 | help="maximum size of height and width for texture images") 21 | parser.add_argument("--resize_content", type=int, default=None, 22 | help="maximum size of height and width for content images") 23 | parser.add_argument("--out_dir", default="output") 24 | parser.add_argument("--no_optimize", dest="optimize", action="store_false") 25 | parser.add_argument("--output_image", default="converted.png") 26 | parser.add_argument("--debug", action="store_true") 27 | parser.add_argument("--debug_span", type=int, default=100) 28 | parser.add_argument("--average_pooling", action="store_true") 29 | parser.add_argument("--model", default="vgg") 30 | parser.add_argument("--modelpath") 31 | parser.add_argument("--random_init", action="store_true") 32 | parser.add_argument("--init_image", default=None) 33 | args = parser.parse_args() 34 | if args.init_image is None: args.init_image = args.content_image 35 | if args.resize_content is None: args.resize_content = args.resize 36 | if args.resize_texture is None: args.resize_texture = args.resize 37 | print(args) 38 | texture_img = neural_art.utility.load_image(args.texture_image) 39 | texture_img = neural_art.utility.resize_img(texture_img, args.resize_texture) 40 | 41 | XSTEP = texture_img.size[0] / args.xsplit 42 | YSTEP = texture_img.size[1] / args.ysplit 43 | texture_imgs = [] 44 | for x_index in range(args.xsplit): 45 | x = x_index * XSTEP 46 | for y_index in range(args.ysplit): 47 | y = y_index * YSTEP 48 | texture_imgs.append(texture_img.crop([x, y, x + XSTEP, y + YSTEP])) 49 | 50 | content_img = neural_art.utility.load_image(args.content_image) 51 | content_img = neural_art.utility.resize_img(content_img, args.resize_content) 52 | init_img = neural_art.utility.load_image(args.init_image) 53 | init_img = neural_art.utility.resize_img(init_img, args.resize_content) 54 | 55 | model = neural_art.utility.load_nn(args.model, modelpath=args.modelpath) 56 | converter = neural_art.image_converters.MultiReferenceImageConverter(texture_imgs, gpu=args.gpu, 57 | content_weight=args.content_weight, 58 | texture_weight=1, model=model, 59 | average_pooling=args.average_pooling) 60 | 61 | if args.debug: 62 | if not os.path.exists(args.out_dir): os.mkdir(args.out_dir) 63 | debug_span = args.debug_span 64 | else: 65 | debug_span = args.iteration * 2 66 | converter.convert_debug(content_img, init_img=init_img, 67 | max_iteration=args.iteration, debug_span=debug_span, output_directory=args.out_dir, 68 | optimize=args.optimize, random_init=args.random_init).save(args.output_image) 69 | -------------------------------------------------------------------------------- /neural_art/image_converters/large_image_converter.py: -------------------------------------------------------------------------------- 1 | import neural_art 2 | import chainer 3 | import chainer.cuda 4 | from . import multi_reference_image_converter 5 | import numpy 6 | import os 7 | 8 | class LargeImageConverter(object): 9 | def __init__(self, texture_imgs, model, gpu, optimizer, content_weight=1, texture_weight=1): 10 | """ 11 | :type converter: multi_reference_image_converter.MultiReferenceImageConverter 12 | """ 13 | self.converter = neural_art.image_converters.MultiReferenceImageConverter( 14 | texture_imgs, gpu=gpu, content_weight=content_weight, texture_weight=1, model=model, average_pooling=True) 15 | self.model = model 16 | self.optimizer = optimizer 17 | self.content_weight = content_weight 18 | self.texture_weight = texture_weight 19 | 20 | if gpu >= 0: 21 | chainer.cuda.get_device(gpu).use() 22 | self.xp = chainer.cuda.cupy 23 | self.model.model.to_gpu() 24 | else: 25 | self.xp = numpy 26 | 27 | def convert_debug(self, content_img, init_img, output_directory, 28 | max_iteration=1000, debug_span=100, random_init=False, 29 | xsplit=3, ysplit=3, overwrap=50, average_pooling=False): 30 | init_array = self.xp.array(neural_art.utility.img2array(init_img)) 31 | content_array = neural_art.utility.img2array(content_img) 32 | if random_init: 33 | init_array = self.xp.random.uniform(-20, 20, init_array.shape, dtype=init_array.dtype) 34 | 35 | subrects = [] 36 | ### (step-wrap)*(split-1) = w-step 37 | xstep = (init_array.shape[2]+(xsplit-1)*overwrap-1) / xsplit 38 | ystep = (init_array.shape[3]+(ysplit-1)*overwrap-1) / ysplit 39 | for x in range(0, init_array.shape[2]-xstep, xstep-overwrap): 40 | for y in range(0, init_array.shape[3]-ystep, ystep-overwrap): 41 | subrects.append((x, y, x+xstep, y+ystep)) 42 | 43 | rects_content_layers = [] 44 | target_texture_ratios = [] 45 | for x1, y1, x2, y2 in subrects: 46 | subimg = self.xp.asarray(content_array[:, :, x1:x2, y1:y2]) 47 | layers = self.model.forward_layers(chainer.Variable(subimg, volatile=True)) 48 | texture_feature = self.converter._to_texture_feature(layers) 49 | target_texture_ratio = self.converter.optimize_texture_feature(texture_feature) 50 | target_texture_ratios.append(target_texture_ratio) 51 | 52 | parameter_now = chainer.links.Parameter(init_array) 53 | self.optimizer.setup(parameter_now) 54 | for i in xrange(max_iteration+1): 55 | neural_art.utility.print_ltsv({"iteration": i}) 56 | if i % debug_span == 0 and i > 0: 57 | print("save") 58 | neural_art.utility.array2img(chainer.cuda.to_cpu(parameter_now.W.data)).save( 59 | os.path.join(output_directory, "{}.png".format(i))) 60 | parameter_now.zerograds() 61 | for (x1, y1, x2, y2), target_texture_ratio in zip(subrects, target_texture_ratios): 62 | subimg = self.xp.asarray(content_array[:, :, x1:x2, y1:y2]) 63 | contents_layers = self.model.forward_layers(chainer.Variable(subimg, volatile=True)) 64 | contents_layers = [ 65 | chainer.Variable(layer.data) for layer in contents_layers 66 | ] 67 | 68 | x = chainer.Variable(self.xp.ascontiguousarray(parameter_now.W.data[:, :, x1:x2, y1:y2])) 69 | layers = self.model.forward_layers(x, average_pooling=average_pooling) 70 | texture_feature = self.converter._to_texture_feature(layers) 71 | target_texture_feature = self.converter._constructed_feature(target_texture_ratio) 72 | loss_texture = self.converter.squared_error( 73 | texture_feature, 74 | target_texture_feature 75 | ) 76 | loss_content = self.converter._contents_loss(layers, contents_layers) 77 | loss = self.texture_weight * loss_texture + self.content_weight * loss_content 78 | loss.backward() 79 | parameter_now.W.grad[:, :, x1:x2, y1:y2] += x.grad 80 | self.optimizer.update() 81 | return neural_art.utility.array2img(chainer.cuda.to_cpu(parameter_now.W.data)) 82 | 83 | 84 | 85 | -------------------------------------------------------------------------------- /neural_art/image_converters/multi_reference_image_converter.py: -------------------------------------------------------------------------------- 1 | import chainer 2 | import chainer.links 3 | import chainer.cuda 4 | import chainer.optimizers 5 | import chainer.functions 6 | import neural_art 7 | import numpy 8 | from . import image_converter 9 | import openopt 10 | from builtins import range 11 | 12 | 13 | class MultiReferenceImageConverter(image_converter.BaseImageConverter): 14 | def __init__(self, texture_imgs, gpu=-1, optimizer=None, model=None, content_weight=1, texture_weight=1, average_pooling=False): 15 | super(MultiReferenceImageConverter, self).__init__(gpu=gpu, optimizer=optimizer, model=model, content_weight=content_weight, texture_weight=texture_weight, average_pooling=average_pooling) 16 | self.texture_features = [] 17 | for texture_img in texture_imgs: 18 | texture_array = self.xp.array(neural_art.utility.img2array(texture_img)) 19 | layers = self.model.forward_layers(chainer.Variable(texture_array), average_pooling=self.average_pooling) 20 | self.texture_features.append(chainer.Variable(self._to_texture_feature(layers).data)) 21 | 22 | def _constructed_feature(self, ratio): 23 | constructed_feature = None 24 | for texture_feature_index in range(len(self.texture_features)): 25 | if constructed_feature is None: 26 | constructed_feature = ratio[texture_feature_index] * self.texture_features[texture_feature_index] 27 | else: 28 | constructed_feature += ratio[texture_feature_index] * self.texture_features[texture_feature_index] 29 | return chainer.Variable(self.xp.array(constructed_feature.data)) 30 | 31 | def convert_debug(self, content_img, init_img, output_directory, max_iteration=1000, debug_span=100, optimize=True, random_init=False): 32 | initial_array = self.xp.array(neural_art.utility.img2array(content_img)) 33 | initial_feature = self._to_texture_feature(self.model.forward_layers(chainer.Variable(initial_array), average_pooling=self.average_pooling)) 34 | if optimize: 35 | self.texture_ratio = self.optimize_texture_feature(initial_feature) 36 | else: 37 | self.texture_ratio = numpy.ones(len(self.texture_features)) / len(self.texture_features) 38 | self.constructed_feature = self._constructed_feature(self.texture_ratio) 39 | for i in range(0, initial_feature.data.shape[0], 10000): 40 | print(i, ":", self.constructed_feature.data[i:i+10000].sum()/initial_feature.data[i:i+10000].sum()) 41 | return super(MultiReferenceImageConverter, self).convert_debug(content_img=content_img, init_img=init_img, output_directory=output_directory, max_iteration=max_iteration, debug_span=debug_span, random_init=random_init) 42 | 43 | def _texture_loss(self, layers): 44 | now_feature = self._to_texture_feature(layers) 45 | debug = True 46 | if debug: 47 | constructed_feature = self._constructed_feature(numpy.ones(len(self.texture_features))/len(self.texture_features)) 48 | loss_texture = self.squared_error( 49 | now_feature, 50 | constructed_feature 51 | ) 52 | print("loss_texture_before", loss_texture.data) 53 | 54 | loss_texture = self.squared_error( 55 | now_feature, 56 | self.constructed_feature 57 | ) 58 | print("loss_texture", loss_texture.data) 59 | return loss_texture 60 | 61 | def optimize_texture_feature(self, target_feature): 62 | """ 63 | minimize (target - k1s1 - k2s2 + ...) ^ 2 64 | 65 | -> 66 | -2 * target * (k1s1 + k2s2) + (k1s1 + k2s2 + ...) ^ 2 67 | 68 | -> 69 | (k1 k2)(s1^2 s1s2, s2s1, s2^2)(k1 k2) + (-2Ts1, -2Ts2)(k1, k2) 70 | """ 71 | num_textures = len(self.texture_features) 72 | H = numpy.zeros((num_textures, num_textures)) 73 | for x in range(num_textures): 74 | for y in range(num_textures): 75 | H[x, y] = 2*self.texture_features[x].data.dot(self.texture_features[y].data) 76 | print("H:", H) 77 | f = numpy.zeros(num_textures) 78 | for x in range(num_textures): 79 | f[x] = -2 * target_feature.data.dot(self.texture_features[x].data) 80 | lower_bound = numpy.zeros(num_textures) # non negative 81 | aeq, beq = numpy.ones(num_textures), 1 # w1+w2+... = 1 82 | p = openopt.QP(H, f, Aeq=aeq, beq=beq, lb=lower_bound) 83 | r = p.solve("cvxopt_qp") 84 | ratio = r.xf 85 | print("Style ratios: ", ratio) 86 | print("sum", self.xp.sum(ratio)) 87 | return ratio 88 | -------------------------------------------------------------------------------- /neural_art/image_converters/image_converter.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | import chainer 3 | import chainer.links 4 | import chainer.cuda 5 | import chainer.optimizers 6 | import chainer.functions 7 | import neural_art 8 | import numpy 9 | import os 10 | from builtins import range 11 | 12 | 13 | class BaseImageConverter(object): 14 | def __init__(self, gpu=-1, optimizer=None, model=None, content_weight=1, texture_weight=1, average_pooling=False): 15 | self.content_weight = content_weight 16 | self.texture_weight = texture_weight 17 | self.average_pooling = average_pooling 18 | if optimizer is None: 19 | self.optimizer = chainer.optimizers.Adam(alpha=4.0) 20 | else: 21 | self.optimizer = optimizer 22 | if model is None: 23 | self.model = neural_art.utility.load_nn("vgg") 24 | else: 25 | self.model = model 26 | 27 | if gpu >= 0: 28 | chainer.cuda.get_device(gpu).use() 29 | self.xp = chainer.cuda.cupy 30 | self.model.model.to_gpu() 31 | else: 32 | self.xp = numpy 33 | 34 | def convert(self, content_img, init_img, iteration=1000): 35 | return self.convert_debug(content_img, init_img, max_iteration=iteration, debug_span=iteration + 1, 36 | output_directory=None) 37 | 38 | def convert_debug(self, content_img, init_img, output_directory, max_iteration=1000, debug_span=100, 39 | random_init=False): 40 | init_array = self.xp.array(neural_art.utility.img2array(init_img)) 41 | if random_init: 42 | init_array = self.xp.array(self.xp.random.uniform(-20, 20, init_array.shape), dtype=init_array.dtype) 43 | content_array = self.xp.array(neural_art.utility.img2array(content_img)) 44 | content_layers = self.model.forward_layers(chainer.Variable(content_array), 45 | average_pooling=self.average_pooling) 46 | 47 | parameter_now = chainer.links.Parameter(init_array) 48 | self.optimizer.setup(parameter_now) 49 | for i in range(max_iteration + 1): 50 | neural_art.utility.print_ltsv({"iteration": i}) 51 | if i % debug_span == 0 and i > 0: 52 | print("dump to {}".format(os.path.join(output_directory, "{}.png".format(i)))) 53 | neural_art.utility.array2img(chainer.cuda.to_cpu(parameter_now.W.data)).save( 54 | os.path.join(output_directory, "{}.png".format(i))) 55 | parameter_now.zerograds() 56 | x = parameter_now.W 57 | layers = self.model.forward_layers(x, average_pooling=self.average_pooling) 58 | 59 | loss_texture = self._texture_loss(layers) 60 | loss_content = self._contents_loss(layers, content_layers) 61 | loss = self.texture_weight * loss_texture + self.content_weight * loss_content 62 | loss.backward() 63 | parameter_now.W.grad = x.grad 64 | self.optimizer.update() 65 | return neural_art.utility.array2img(chainer.cuda.to_cpu(parameter_now.W.data)) 66 | 67 | def _contents_loss(self, layers, content_layers): 68 | """ 69 | calculate content difference between original & processing 70 | """ 71 | loss_contents = chainer.Variable(self.xp.zeros((), dtype=numpy.float32)) 72 | for layer_index in range(len(layers)): 73 | loss_contents += numpy.float32(self.model.alpha[layer_index]) * chainer.functions.mean_squared_error( 74 | layers[layer_index], 75 | content_layers[layer_index]) 76 | return loss_contents 77 | 78 | def _texture_loss(self, layers): 79 | """ 80 | :param layers: predicted value of each layer 81 | :type layers: List[chainer.Variable] 82 | """ 83 | raise Exception("Not implemented") 84 | 85 | def _to_texture_feature(self, layers): 86 | """ 87 | :param layers: predicted value of each layer 88 | :type layers: List[chainer.Variable] 89 | """ 90 | subvectors = [] 91 | for layer_index in range(len(layers)): 92 | layer = layers[layer_index] 93 | beta = numpy.sqrt(numpy.float32(self.model.beta[layer_index]) / len(layers)) 94 | texture_matrix = float(beta) * neural_art.utility.get_matrix(layer) 95 | texture_matrix /= numpy.sqrt(numpy.prod(texture_matrix.data.shape)) # normalize 96 | subvector = chainer.functions.reshape(texture_matrix, (numpy.prod(texture_matrix.data.shape),)) 97 | subvectors.append(subvector) 98 | return chainer.functions.concat(subvectors, axis=0) 99 | 100 | def squared_error(self, f1, f2): 101 | loss = chainer.functions.sum((f1 - f2) * (f1 - f2)) 102 | return loss 103 | 104 | 105 | class ImageConverterMatrix(BaseImageConverter): 106 | """ 107 | Neural art converter (naive implementation) 108 | just for comparison 109 | """ 110 | 111 | def __init__(self, texture_img, gpu=-1, optimizer=None, model=None, content_weight=1, texture_weight=1): 112 | super(ImageConverterMatrix, self).__init__(gpu=gpu, optimizer=optimizer, model=model, 113 | content_weight=content_weight, texture_weight=texture_weight) 114 | texture_array = self.xp.array(neural_art.utility.img2array(texture_img)) 115 | self.texture_matrices = [neural_art.utility.get_matrix(layer) for layer in 116 | self.model.forward_layers(chainer.Variable(texture_array), 117 | average_pooling=self.average_pooling)] 118 | 119 | def _texture_loss(self, layers): 120 | loss_texture = chainer.Variable(self.xp.zeros((), dtype=self.xp.float32)) 121 | for layer_index in range(len(layers)): 122 | matrix = neural_art.utility.get_matrix(layers[layer_index]) 123 | loss = self.xp.float32(self.model.beta[layer_index]) * chainer.functions.mean_squared_error( 124 | matrix, 125 | self.texture_matrices[layer_index] 126 | ) / self.xp.float32(len(layers)) 127 | loss_texture += loss 128 | print("loss_texture", loss_texture.data) 129 | return loss_texture 130 | 131 | 132 | class ImageConverter(BaseImageConverter): 133 | """ 134 | Neural art converter with large texture feature vector 135 | """ 136 | 137 | def __init__(self, texture_img, gpu=-1, optimizer=None, model=None, content_weight=1, texture_weight=1): 138 | super(ImageConverter, self).__init__(gpu=gpu, optimizer=optimizer, model=model, content_weight=content_weight, 139 | texture_weight=texture_weight) 140 | texture_array = self.xp.array(neural_art.utility.img2array(texture_img)) 141 | self.texture_feature = self._to_texture_feature( 142 | self.model.forward_layers(chainer.Variable(texture_array), average_pooling=self.average_pooling)) 143 | 144 | def _texture_loss(self, layers): 145 | original_feature = self._to_texture_feature(layers) 146 | loss_texture = self.squared_error( 147 | original_feature, 148 | self.texture_feature 149 | ) 150 | print("loss_texture_feature", loss_texture.data) 151 | return loss_texture 152 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) 2016 DWANGO Co., Ltd. 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | neural_style_synthesizer Copyright (C) 2016 DWANGO Co., Ltd. 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------