├── .gitattributes ├── .gitignore ├── LICENSE ├── README.md ├── sketch_detector ├── __init__.py ├── helper.py ├── sketch_detector.py ├── sketch_pytorch.py └── sketch_pytorch_weight.npy ├── sketch_keras.jpg ├── sketch_pytorch.jpg ├── test.py └── test_case.jpg /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # celery beat schedule file 95 | celerybeat-schedule 96 | 97 | # SageMath parsed files 98 | *.sage.py 99 | 100 | # Environments 101 | .env 102 | .venv 103 | env/ 104 | venv/ 105 | ENV/ 106 | env.bak/ 107 | venv.bak/ 108 | 109 | # Spyder project settings 110 | .spyderproject 111 | .spyproject 112 | 113 | # Rope project settings 114 | .ropeproject 115 | 116 | # mkdocs documentation 117 | /site 118 | 119 | # mypy 120 | .mypy_cache/ 121 | .dmypy.json 122 | dmypy.json 123 | 124 | # Pyre type checker 125 | .pyre/ 126 | -------------------------------------------------------------------------------- /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 license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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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 | Copyright (C) 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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Sketch Pytorch 2 | 3 | A pretrained model which can convert an anime image to a sketch. 4 | 5 | Thanks for the work of [lllyasviel](https://github.com/lllyasviel), here's [sketchKeras](https://github.com/lllyasviel/sketchKeras). 6 | 7 | I just convert sketchKeras to a pytorch project. 8 | 9 | And I also appreciate [MMdnn](https://github.com/microsoft/MMdnn), this project helps me a lot. 10 | 11 | 12 | 13 | ## Requirement 14 | 15 | - Pytorch 16 | 17 | - Opencv 18 | 19 | - Numpy 20 | 21 | 22 | 23 | ## Performance 24 | 25 | We can assume there's no difference between sketchKeras and sketch_pytorch. 26 | 27 | I didn't make a strict test, maybe there's some very small differences, but I think it's not a big deal. 28 | 29 | original image 30 | 31 | sketchkeras 32 | 33 | sketch_pytorch 34 | 35 | 36 | 37 | ## Usage 38 | 39 | Put the folder ```./sketch_detector``` to your project, and use it as use a package. 40 | 41 | ```python 42 | import cv2 43 | import sketch_detector 44 | 45 | img = cv2.imread("test_case.jpg") 46 | result = sketch_detector.get_sketch(img) 47 | cv2.imshow("sketch_pytorch",result) 48 | cv2.waitKey() 49 | ``` 50 | 51 | As alternatives, I also provide ```get_enhanced_sketch```, ```get_colored_sketch``` and ```get_pured_sketch```. Please see the differences on sketchKeras. 52 | 53 | **Moreover, I hard-coded cuda mode. So, if you want to run this project on a cpu, you can delete ```.cuda()``` on line 11 and line 45 of ```sketch_detector.py```** 54 | 55 | 56 | 57 | ## How to convert a keras model to a pytorch model 58 | 59 | Basically, you can follow steps on MMdnn, but I highly recommend you to convert a model step-by-step, don't use ```mmconvert``` directly. 60 | 61 | 1. Convert your model to IR files 62 | 63 | ```powershell 64 | mmtoir -f keras -iw model.h5 -in model.json -o ir 65 | ``` 66 | 67 | You can get your h5 file by ```model.save_weights(your_path)```, and get your json file by ```model.to_json()```. Then, you'll get ```ir.npy```, ```ir.pd```, ```ir.json```. 68 | 69 | 2. Convert IR files to Pytorch code snippet 70 | 71 | ```powershell 72 | mmtocode -f pytorch -in ir.pb -iw ir.npy -o model.py -ow weight.pkl 73 | ``` 74 | 75 | 3. Edit ```model.py``` 76 | 77 | Because of the compatibility, you may need modify some layers by your self. Please see the output of ```mmtocode```. 78 | 79 | e.g. 80 | 81 | ``` 82 | Parse file [ir.pb] with binary format successfully. 83 | Pytorch Emitter has not supported operator [UpSampling2D]. 84 | up_sampling2d_1 85 | Pytorch Emitter has not supported operator [UpSampling2D]. 86 | up_sampling2d_2 87 | Pytorch Emitter has not supported operator [UpSampling2D]. 88 | up_sampling2d_3 89 | Pytorch Emitter has not supported operator [UpSampling2D]. 90 | up_sampling2d_4 91 | Target network code snippet is saved as [pytorch_model.py]. 92 | Target weights are saved as [pytorch_model_weight.pkl]. 93 | ``` 94 | Besides, please take your notice on the shapes of weights, you need to use ```torch.permute()``` or ```np.transpose()``` to change them. 95 | Finally, you can choose a fancy name to replace the default name("KitModel") of this module. 96 | 97 | 98 | 4. Load your model ```model = KitModel("ir.npy")``` 99 | 100 | ### Important hint 101 | 102 | You may need to set ```allow_pickle=True``` on ```model.py``` or you will meet a exception. 103 | 104 | According to stackoverflow comments, It only happens in the new version of numpy. -------------------------------------------------------------------------------- /sketch_detector/__init__.py: -------------------------------------------------------------------------------- 1 | from .sketch_detector import get_colored_sketch, get_enhanced_sketch, get_pured_sketch, get_sketch -------------------------------------------------------------------------------- /sketch_detector/helper.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | from scipy import ndimage 3 | import numpy as np 4 | 5 | def get_normal_map(img): 6 | img = img.astype(np.float) 7 | img = img / 255.0 8 | img = - img + 1 9 | img[img < 0] = 0 10 | img[img > 1] = 1 11 | return img 12 | 13 | def get_gray_map(img): 14 | gray = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2GRAY) 15 | highPass = gray.astype(np.float) 16 | highPass = highPass / 255.0 17 | highPass = 1 - highPass 18 | highPass = highPass[None] 19 | return highPass.transpose((1,2,0)) 20 | 21 | def get_light_map(img): 22 | gray = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2GRAY) 23 | blur = cv2.GaussianBlur(gray, (0, 0), 3) 24 | highPass = gray.astype(int) - blur.astype(int) 25 | highPass = highPass.astype(np.float) 26 | highPass = highPass / 128.0 27 | highPass = highPass[None] 28 | return highPass.transpose((1,2,0)) 29 | 30 | def get_light_map_single(img): 31 | gray = img 32 | gray = gray[None] 33 | gray = gray.transpose((1,2,0)) 34 | blur = cv2.GaussianBlur(gray, (0, 0), 3) 35 | gray = gray.reshape((gray.shape[0],gray.shape[1])) 36 | highPass = gray.astype(int) - blur.astype(int) 37 | highPass = highPass.astype(np.float) 38 | highPass = highPass / 128.0 39 | return highPass 40 | 41 | def get_light_map_drawer(img): 42 | gray = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2GRAY) 43 | blur = cv2.GaussianBlur(gray, (0, 0), 3) 44 | highPass = gray.astype(int) - blur.astype(int) + 255 45 | highPass[highPass < 0 ] = 0 46 | highPass[highPass > 255] = 255 47 | highPass = highPass.astype(np.float) 48 | highPass = highPass / 255.0 49 | highPass = 1 - highPass 50 | highPass = highPass[None] 51 | return highPass.transpose((1,2,0)) 52 | 53 | def get_light_map_drawer2(img): 54 | ret = img.copy() 55 | ret=ret.astype(np.float) 56 | ret[:, :, 0] = get_light_map_drawer3(img[:, :, 0]) 57 | ret[:, :, 1] = get_light_map_drawer3(img[:, :, 1]) 58 | ret[:, :, 2] = get_light_map_drawer3(img[:, :, 2]) 59 | ret = np.amax(ret, 2) 60 | return ret 61 | 62 | def get_light_map_drawer3(img): 63 | gray = img 64 | blur = cv2.blur(gray,ksize=(5,5)) 65 | highPass = gray.astype(int) - blur.astype(int) + 255 66 | highPass[highPass < 0 ] = 0 67 | highPass[highPass > 255] = 255 68 | highPass = highPass.astype(np.float) 69 | highPass = highPass / 255.0 70 | highPass = 1 - highPass 71 | return highPass 72 | 73 | def normalize_pic(img): 74 | img = img / np.max(img) 75 | return img 76 | 77 | def superlize_pic(img): 78 | img = img * 2.33333 79 | img[img > 1] = 1 80 | return img 81 | 82 | def mask_pic(img,mask): 83 | mask_mat = mask 84 | mask_mat = mask_mat.astype(np.float) 85 | mask_mat = cv2.GaussianBlur(mask_mat, (0, 0), 1) 86 | mask_mat = mask_mat / np.max(mask_mat) 87 | mask_mat = mask_mat * 255 88 | mask_mat[mask_mat<255] = 0 89 | mask_mat = mask_mat.astype(np.uint8) 90 | mask_mat = cv2.GaussianBlur(mask_mat, (0, 0), 3) 91 | mask_mat = get_gray_map(mask_mat) 92 | mask_mat = normalize_pic(mask_mat) 93 | mask_mat = resize_img_512(mask_mat) 94 | super_from = np.multiply(img, mask_mat) 95 | return super_from 96 | 97 | def resize_img_512(img): 98 | zeros = np.zeros((512,512,img.shape[2]), dtype=np.float) 99 | zeros[:img.shape[0], :img.shape[1]] = img 100 | return zeros 101 | 102 | def resize_img_512_3d(img): 103 | zeros = np.zeros((3,1,512,512), dtype=np.float) 104 | zeros[0 : img.shape[0] , 0 , 0 : img.shape[1] , 0 : img.shape[2]] = img 105 | return zeros 106 | 107 | def show_active_img_and_save(name,img,path): 108 | mat = get_colored_sketch(img) 109 | cv2.imshow(name,mat) 110 | cv2.imwrite(path,mat) 111 | return 112 | 113 | def denoise_mat(img,i): 114 | return ndimage.median_filter(img, i) 115 | 116 | def show_active_img_and_save_denoise(name,img,path): 117 | mat = get_sketch(img) 118 | cv2.imshow(name,mat) 119 | cv2.imwrite(path,mat) 120 | return 121 | 122 | def show_active_img_and_save_denoise_filter(name,img,path): 123 | mat = get_pured_sketch(img) 124 | cv2.imshow(name,mat) 125 | cv2.imwrite(path,mat) 126 | return 127 | 128 | def show_active_img_and_save_denoise_filter2(name,img,path): 129 | mat = get_enhanced_sketch(img) 130 | cv2.imshow(name,mat) 131 | cv2.imwrite(path,mat) 132 | return 133 | 134 | def show_active_img(name,img): 135 | mat = img.astype(np.float) 136 | mat = - mat + 1 137 | mat = mat * 255.0 138 | mat[mat < 0] = 0 139 | mat[mat > 255] = 255 140 | mat = mat.astype(np.uint8) 141 | cv2.imshow(name,mat) 142 | return 143 | 144 | def get_active_img(img): 145 | mat = img.astype(np.float) 146 | mat = - mat + 1 147 | mat = mat * 255.0 148 | mat[mat < 0] = 0 149 | mat[mat > 255] = 255 150 | mat = mat.astype(np.uint8) 151 | return mat 152 | 153 | def get_active_img_fil(img): 154 | mat = img.astype(np.float) 155 | mat[mat < 0.18] = 0 156 | mat = - mat + 1 157 | mat = mat * 255.0 158 | mat[mat < 0] = 0 159 | mat[mat > 255] = 255 160 | mat = mat.astype(np.uint8) 161 | return mat 162 | 163 | def show_double_active_img(name,img): 164 | mat = img.astype(np.float) 165 | mat = mat * 128.0 166 | mat = mat + 127.0 167 | mat[mat < 0] = 0 168 | mat[mat > 255] = 255 169 | cv2.imshow(name,mat.astype(np.uint8)) 170 | return 171 | 172 | def debug_pic_helper(): 173 | for index in range(1130): 174 | gray_path = 'data\\gray\\'+str(index)+'.jpg' 175 | color_path = 'data\\color\\' + str(index) + '.jpg' 176 | 177 | mat_color = cv2.imread(color_path) 178 | mat_color=get_light_map(mat_color) 179 | mat_color=normalize_pic(mat_color) 180 | mat_color=resize_img_512(mat_color) 181 | show_double_active_img('mat_color',mat_color) 182 | 183 | mat_gray = cv2.imread(gray_path) 184 | mat_gray=get_gray_map(mat_gray) 185 | mat_gray=normalize_pic(mat_gray) 186 | mat_gray = resize_img_512(mat_gray) 187 | show_active_img('mat_gray',mat_gray) 188 | 189 | cv2.waitKey(1000) 190 | 191 | def get_colored_sketch(img): 192 | mat = img.astype(np.float) 193 | mat = - mat + 1 194 | mat = mat * 255.0 195 | mat[mat < 0] = 0 196 | mat[mat > 255] = 255 197 | mat = mat.astype(np.uint8) 198 | return mat 199 | 200 | def get_enhanced_sketch(img): 201 | mat = img.astype(np.float) 202 | mat[mat<0.1] = 0 203 | mat = - mat + 1 204 | mat = mat * 255.0 205 | mat[mat < 0] = 0 206 | mat[mat > 255] = 255 207 | mat = mat.astype(np.uint8) 208 | mat = ndimage.median_filter(mat, 1) 209 | return mat 210 | 211 | def get_pured_sketch(img): 212 | mat = img.astype(np.float) 213 | mat[mat<0.18] = 0 214 | mat = - mat + 1 215 | mat = mat * 255.0 216 | mat[mat < 0] = 0 217 | mat[mat > 255] = 255 218 | mat = mat.astype(np.uint8) 219 | mat = ndimage.median_filter(mat, 1) 220 | return mat 221 | 222 | def get_sketch(img): 223 | mat = img.astype(np.float) 224 | mat = - mat + 1 225 | mat = mat * 255.0 226 | mat[mat < 0] = 0 227 | mat[mat > 255] = 255 228 | mat = mat.astype(np.uint8) 229 | mat = ndimage.median_filter(mat, 1) 230 | return mat -------------------------------------------------------------------------------- /sketch_detector/sketch_detector.py: -------------------------------------------------------------------------------- 1 | import os 2 | import cv2 3 | import numpy as np 4 | import torch 5 | import torchvision.transforms as transforms 6 | from . import helper 7 | from .sketch_pytorch import sketch_pytorch 8 | 9 | 10 | mod = sketch_pytorch(os.path.split(os.path.realpath(__file__))[0] + '/sketch_pytorch_weight.npy') 11 | mod.cuda() 12 | mod.eval() 13 | 14 | def get(path): 15 | img = cv2.imread(path) 16 | line_mat = pre_process(img) 17 | helper.show_active_img_and_save('sketchKeras_colored', line_mat, 'sketchKeras_colored.jpg') 18 | line_mat = np.amax(line_mat, 2) 19 | helper.show_active_img_and_save_denoise_filter2('sketchKeras_enhanced', line_mat, 'sketchKeras_enhanced.jpg') 20 | helper.show_active_img_and_save_denoise_filter('sketchKeras_pured', line_mat, 'sketchKeras_pured.jpg') 21 | helper.show_active_img_and_save_denoise('sketchKeras', line_mat, 'sketchKeras.jpg') 22 | cv2.waitKey(0) 23 | return 24 | 25 | def pre_process(img): 26 | width = float(img.shape[1]) 27 | height = float(img.shape[0]) 28 | new_width = 0 29 | new_height = 0 30 | if (width > height): 31 | img = cv2.resize(img, (512, int(512 / width * height)), interpolation=cv2.INTER_AREA) 32 | new_width = 512 33 | new_height = int(512 / width * height) 34 | else: 35 | img = cv2.resize(img, (int(512 / height * width), 512), interpolation=cv2.INTER_AREA) 36 | new_width = int(512 / height * width) 37 | new_height = 512 38 | 39 | img = img.transpose((2, 0, 1)) 40 | light_map = np.zeros(img.shape, dtype=np.float) 41 | for channel in range(3): 42 | light_map[channel] = helper.get_light_map_single(img[channel]) 43 | light_map = helper.normalize_pic(light_map) 44 | light_map = helper.resize_img_512_3d(light_map) 45 | line_mat = mod(torch.tensor(light_map, dtype=torch.float).cuda()) 46 | line_mat = line_mat.permute((1, 2, 3, 0)).cpu().detach().numpy()[0] 47 | line_mat = line_mat[0:int(new_height), 0:int(new_width), :] 48 | 49 | return line_mat 50 | 51 | def get_colored_sketch(img): 52 | img = pre_process(img) 53 | return helper.get_colored_sketch(img).reshape(img.shape[0], img.shape[1], 1) 54 | 55 | def get_pured_sketch(img): 56 | img = pre_process(img) 57 | img = np.amax(img, 2) 58 | return helper.get_pured_sketch(img).reshape(img.shape[0], img.shape[1], 1) 59 | 60 | def get_enhanced_sketch(img): 61 | img = pre_process(img) 62 | img = np.amax(img, 2) 63 | return helper.get_enhanced_sketch(img).reshape(img.shape[0], img.shape[1], 1) 64 | 65 | def get_sketch(img): 66 | img = pre_process(img) 67 | img = np.amax(img, 2) 68 | return helper.get_sketch(img).reshape(img.shape[0], img.shape[1], 1) -------------------------------------------------------------------------------- /sketch_detector/sketch_pytorch.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | import torch.nn as nn 4 | import torch.nn.functional as F 5 | import math 6 | 7 | __weights_dict = dict() 8 | 9 | def load_weights(weight_file): 10 | if weight_file == None: 11 | return 12 | 13 | try: 14 | weights_dict = np.load(weight_file).item() 15 | except: 16 | weights_dict = np.load(weight_file, encoding='bytes').item() 17 | 18 | return weights_dict 19 | 20 | class sketch_pytorch(nn.Module): 21 | def __init__(self, weight_file): 22 | super(sketch_pytorch, self).__init__() 23 | global __weights_dict 24 | __weights_dict = load_weights(weight_file) 25 | 26 | self.conv2d_1 = self.__conv(2, name='conv2d_1', in_channels=1, out_channels=32, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True) 27 | self.batch_normalization_1 = self.__batch_normalization(2, 'batch_normalization_1', num_features=32, eps=0.0010000000474974513, momentum=0.0) 28 | self.conv2d_2 = self.__conv(2, name='conv2d_2', in_channels=32, out_channels=64, kernel_size=(4, 4), stride=(2, 2), groups=1, bias=True) 29 | self.batch_normalization_2 = self.__batch_normalization(2, 'batch_normalization_2', num_features=64, eps=0.0010000000474974513, momentum=0.0) 30 | self.conv2d_3 = self.__conv(2, name='conv2d_3', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True) 31 | self.batch_normalization_3 = self.__batch_normalization(2, 'batch_normalization_3', num_features=64, eps=0.0010000000474974513, momentum=0.0) 32 | self.conv2d_4 = self.__conv(2, name='conv2d_4', in_channels=64, out_channels=128, kernel_size=(4, 4), stride=(2, 2), groups=1, bias=True) 33 | self.batch_normalization_4 = self.__batch_normalization(2, 'batch_normalization_4', num_features=128, eps=0.0010000000474974513, momentum=0.0) 34 | self.conv2d_5 = self.__conv(2, name='conv2d_5', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True) 35 | self.batch_normalization_5 = self.__batch_normalization(2, 'batch_normalization_5', num_features=128, eps=0.0010000000474974513, momentum=0.0) 36 | self.conv2d_6 = self.__conv(2, name='conv2d_6', in_channels=128, out_channels=256, kernel_size=(4, 4), stride=(2, 2), groups=1, bias=True) 37 | self.batch_normalization_6 = self.__batch_normalization(2, 'batch_normalization_6', num_features=256, eps=0.0010000000474974513, momentum=0.0) 38 | self.conv2d_7 = self.__conv(2, name='conv2d_7', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True) 39 | self.batch_normalization_7 = self.__batch_normalization(2, 'batch_normalization_7', num_features=256, eps=0.0010000000474974513, momentum=0.0) 40 | self.conv2d_8 = self.__conv(2, name='conv2d_8', in_channels=256, out_channels=512, kernel_size=(4, 4), stride=(2, 2), groups=1, bias=True) 41 | self.batch_normalization_8 = self.__batch_normalization(2, 'batch_normalization_8', num_features=512, eps=0.0010000000474974513, momentum=0.0) 42 | self.conv2d_9 = self.__conv(2, name='conv2d_9', in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True) 43 | self.batch_normalization_9 = self.__batch_normalization(2, 'batch_normalization_9', num_features=512, eps=0.0010000000474974513, momentum=0.0) 44 | self.conv2d_10 = self.__conv(2, name='conv2d_10', in_channels=1024, out_channels=512, kernel_size=(4, 4), stride=(1, 1), groups=1, bias=True) 45 | self.batch_normalization_10 = self.__batch_normalization(2, 'batch_normalization_10', num_features=512, eps=0.0010000000474974513, momentum=0.0) 46 | self.conv2d_11 = self.__conv(2, name='conv2d_11', in_channels=512, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True) 47 | self.batch_normalization_11 = self.__batch_normalization(2, 'batch_normalization_11', num_features=256, eps=0.0010000000474974513, momentum=0.0) 48 | self.conv2d_12 = self.__conv(2, name='conv2d_12', in_channels=512, out_channels=256, kernel_size=(4, 4), stride=(1, 1), groups=1, bias=True) 49 | self.batch_normalization_12 = self.__batch_normalization(2, 'batch_normalization_12', num_features=256, eps=0.0010000000474974513, momentum=0.0) 50 | self.conv2d_13 = self.__conv(2, name='conv2d_13', in_channels=256, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True) 51 | self.batch_normalization_13 = self.__batch_normalization(2, 'batch_normalization_13', num_features=128, eps=0.0010000000474974513, momentum=0.0) 52 | self.conv2d_14 = self.__conv(2, name='conv2d_14', in_channels=256, out_channels=128, kernel_size=(4, 4), stride=(1, 1), groups=1, bias=True) 53 | self.batch_normalization_14 = self.__batch_normalization(2, 'batch_normalization_14', num_features=128, eps=0.0010000000474974513, momentum=0.0) 54 | self.conv2d_15 = self.__conv(2, name='conv2d_15', in_channels=128, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True) 55 | self.batch_normalization_15 = self.__batch_normalization(2, 'batch_normalization_15', num_features=64, eps=0.0010000000474974513, momentum=0.0) 56 | self.conv2d_16 = self.__conv(2, name='conv2d_16', in_channels=128, out_channels=64, kernel_size=(4, 4), stride=(1, 1), groups=1, bias=True) 57 | self.batch_normalization_16 = self.__batch_normalization(2, 'batch_normalization_16', num_features=64, eps=0.0010000000474974513, momentum=0.0) 58 | self.conv2d_17 = self.__conv(2, name='conv2d_17', in_channels=64, out_channels=32, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True) 59 | self.batch_normalization_17 = self.__batch_normalization(2, 'batch_normalization_17', num_features=32, eps=0.0010000000474974513, momentum=0.0) 60 | self.conv2d_18 = self.__conv(2, name='conv2d_18', in_channels=64, out_channels=1, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=True) 61 | 62 | def forward(self, x): 63 | conv2d_1_pad = F.pad(x, (1, 1, 1, 1)) 64 | conv2d_1 = self.conv2d_1(conv2d_1_pad) 65 | batch_normalization_1 = self.batch_normalization_1(conv2d_1) 66 | activation_1 = F.relu(batch_normalization_1) 67 | conv2d_2_pad = F.pad(activation_1, (1, 1, 1, 1)) 68 | conv2d_2 = self.conv2d_2(conv2d_2_pad) 69 | batch_normalization_2 = self.batch_normalization_2(conv2d_2) 70 | activation_2 = F.relu(batch_normalization_2) 71 | conv2d_3_pad = F.pad(activation_2, (1, 1, 1, 1)) 72 | conv2d_3 = self.conv2d_3(conv2d_3_pad) 73 | batch_normalization_3 = self.batch_normalization_3(conv2d_3) 74 | activation_3 = F.relu(batch_normalization_3) 75 | conv2d_4_pad = F.pad(activation_3, (1, 1, 1, 1)) 76 | conv2d_4 = self.conv2d_4(conv2d_4_pad) 77 | batch_normalization_4 = self.batch_normalization_4(conv2d_4) 78 | activation_4 = F.relu(batch_normalization_4) 79 | conv2d_5_pad = F.pad(activation_4, (1, 1, 1, 1)) 80 | conv2d_5 = self.conv2d_5(conv2d_5_pad) 81 | batch_normalization_5 = self.batch_normalization_5(conv2d_5) 82 | activation_5 = F.relu(batch_normalization_5) 83 | conv2d_6_pad = F.pad(activation_5, (1, 1, 1, 1)) 84 | conv2d_6 = self.conv2d_6(conv2d_6_pad) 85 | batch_normalization_6 = self.batch_normalization_6(conv2d_6) 86 | activation_6 = F.relu(batch_normalization_6) 87 | conv2d_7_pad = F.pad(activation_6, (1, 1, 1, 1)) 88 | conv2d_7 = self.conv2d_7(conv2d_7_pad) 89 | batch_normalization_7 = self.batch_normalization_7(conv2d_7) 90 | activation_7 = F.relu(batch_normalization_7) 91 | conv2d_8_pad = F.pad(activation_7, (1, 1, 1, 1)) 92 | conv2d_8 = self.conv2d_8(conv2d_8_pad) 93 | batch_normalization_8 = self.batch_normalization_8(conv2d_8) 94 | activation_8 = F.relu(batch_normalization_8) 95 | conv2d_9_pad = F.pad(activation_8, (1, 1, 1, 1)) 96 | conv2d_9 = self.conv2d_9(conv2d_9_pad) 97 | batch_normalization_9 = self.batch_normalization_9(conv2d_9) 98 | activation_9 = F.relu(batch_normalization_9) 99 | concatenate_1 = torch.cat((activation_8, activation_9), 1) 100 | conv2d_10_pad = F.pad(nn.Upsample(scale_factor=2)(concatenate_1), (1, 2, 1, 2)) 101 | conv2d_10 = self.conv2d_10(conv2d_10_pad) 102 | batch_normalization_10 = self.batch_normalization_10(conv2d_10) 103 | activation_10 = F.relu(batch_normalization_10) 104 | conv2d_11_pad = F.pad(activation_10, (1, 1, 1, 1)) 105 | conv2d_11 = self.conv2d_11(conv2d_11_pad) 106 | batch_normalization_11 = self.batch_normalization_11(conv2d_11) 107 | activation_11 = F.relu(batch_normalization_11) 108 | concatenate_2 = torch.cat((activation_7, activation_11), 1) 109 | conv2d_12_pad = F.pad(nn.Upsample(scale_factor=2)(concatenate_2), (1, 2, 1, 2)) 110 | conv2d_12 = self.conv2d_12(conv2d_12_pad) 111 | batch_normalization_12 = self.batch_normalization_12(conv2d_12) 112 | activation_12 = F.relu(batch_normalization_12) 113 | conv2d_13_pad = F.pad(activation_12, (1, 1, 1, 1)) 114 | conv2d_13 = self.conv2d_13(conv2d_13_pad) 115 | batch_normalization_13 = self.batch_normalization_13(conv2d_13) 116 | activation_13 = F.relu(batch_normalization_13) 117 | concatenate_3 = torch.cat((activation_5, activation_13), 1) 118 | conv2d_14_pad = F.pad(nn.Upsample(scale_factor=2)(concatenate_3), (1, 2, 1, 2)) 119 | conv2d_14 = self.conv2d_14(conv2d_14_pad) 120 | batch_normalization_14 = self.batch_normalization_14(conv2d_14) 121 | activation_14 = F.relu(batch_normalization_14) 122 | conv2d_15_pad = F.pad(activation_14, (1, 1, 1, 1)) 123 | conv2d_15 = self.conv2d_15(conv2d_15_pad) 124 | batch_normalization_15 = self.batch_normalization_15(conv2d_15) 125 | activation_15 = F.relu(batch_normalization_15) 126 | concatenate_4 = torch.cat((activation_3, activation_15), 1) 127 | conv2d_16_pad = F.pad(nn.Upsample(scale_factor=2)(concatenate_4), (1, 2, 1, 2)) 128 | conv2d_16 = self.conv2d_16(conv2d_16_pad) 129 | batch_normalization_16 = self.batch_normalization_16(conv2d_16) 130 | activation_16 = F.relu(batch_normalization_16) 131 | conv2d_17_pad = F.pad(activation_16, (1, 1, 1, 1)) 132 | conv2d_17 = self.conv2d_17(conv2d_17_pad) 133 | batch_normalization_17 = self.batch_normalization_17(conv2d_17) 134 | activation_17 = F.relu(batch_normalization_17) 135 | concatenate_5 = torch.cat((activation_1, activation_17), 1) 136 | conv2d_18_pad = F.pad(concatenate_5, (1, 1, 1, 1)) 137 | conv2d_18 = self.conv2d_18(conv2d_18_pad) 138 | return conv2d_18 139 | 140 | 141 | @staticmethod 142 | def __batch_normalization(dim, name, **kwargs): 143 | if dim == 0 or dim == 1: layer = nn.BatchNorm1d(**kwargs) 144 | elif dim == 2: layer = nn.BatchNorm2d(**kwargs) 145 | elif dim == 3: layer = nn.BatchNorm3d(**kwargs) 146 | else: raise NotImplementedError() 147 | 148 | if 'scale' in __weights_dict[name]: 149 | layer.state_dict()['weight'].copy_(torch.from_numpy(__weights_dict[name]['scale'])) 150 | else: 151 | layer.weight.data.fill_(1) 152 | 153 | if 'bias' in __weights_dict[name]: 154 | layer.state_dict()['bias'].copy_(torch.from_numpy(__weights_dict[name]['bias'])) 155 | else: 156 | layer.bias.data.fill_(0) 157 | 158 | layer.state_dict()['running_mean'].copy_(torch.from_numpy(__weights_dict[name]['mean'])) 159 | layer.state_dict()['running_var'].copy_(torch.from_numpy(__weights_dict[name]['var'])) 160 | return layer 161 | 162 | @staticmethod 163 | def __conv(dim, name, **kwargs): 164 | if dim == 1: layer = nn.Conv1d(**kwargs) 165 | elif dim == 2: layer = nn.Conv2d(**kwargs) 166 | elif dim == 3: layer = nn.Conv3d(**kwargs) 167 | else: raise NotImplementedError() 168 | 169 | layer.state_dict()['weight'].copy_(torch.from_numpy(__weights_dict[name]['weights']).permute((3,2,0,1))) 170 | if 'bias' in __weights_dict[name]: 171 | layer.state_dict()['bias'].copy_(torch.from_numpy(__weights_dict[name]['bias'])) 172 | return layer -------------------------------------------------------------------------------- /sketch_detector/sketch_pytorch_weight.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fishjump/sketchPytorch/66c541863baab1ad03bb514d6b143c2e1d1813d7/sketch_detector/sketch_pytorch_weight.npy -------------------------------------------------------------------------------- /sketch_keras.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fishjump/sketchPytorch/66c541863baab1ad03bb514d6b143c2e1d1813d7/sketch_keras.jpg -------------------------------------------------------------------------------- /sketch_pytorch.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fishjump/sketchPytorch/66c541863baab1ad03bb514d6b143c2e1d1813d7/sketch_pytorch.jpg -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import sketch_detector 2 | import cv2 3 | 4 | img = cv2.imread("test_case.jpg") 5 | result = sketch_detector.get_sketch(img) 6 | cv2.imshow("sketch_pytorch",result) 7 | cv2.waitKey() -------------------------------------------------------------------------------- /test_case.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fishjump/sketchPytorch/66c541863baab1ad03bb514d6b143c2e1d1813d7/test_case.jpg --------------------------------------------------------------------------------