├── imdenoising ├── __init__.py ├── utils │ ├── __init__.py │ ├── tensorutils.py │ ├── imutils.py │ └── layerutils.py └── deepimprior.py ├── images └── deep-image-prior-model.png ├── .gitignore ├── README.md └── LICENSE /imdenoising/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /imdenoising/utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /images/deep-image-prior-model.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/D-K-E/image-denoising-tf/master/images/deep-image-prior-model.png -------------------------------------------------------------------------------- /imdenoising/utils/tensorutils.py: -------------------------------------------------------------------------------- 1 | """! 2 | \file tensorutils.py Functions involving creation of different type of tensors 3 | """ 4 | import numpy as np 5 | import tensorflow as tf 6 | 7 | from typing import Tuple, List 8 | 9 | 10 | def random_tensor(ishape: List[int], min_v, max_v, dtype): 11 | """! 12 | \brief creates a random tensor of given shape 13 | """ 14 | if any([a < 1 for a in ishape]): 15 | raise ValueError("input shape can not have an element less than 1") 16 | arr = np.random.rand(*ishape) 17 | arr = min_v + (max_v - min_v) * arr 18 | return tf.constant(arr, dtype=dtype) 19 | -------------------------------------------------------------------------------- /imdenoising/utils/imutils.py: -------------------------------------------------------------------------------- 1 | """! 2 | \file imutils.py image utils 3 | """ 4 | 5 | from PIL import Image 6 | import numpy as np 7 | 8 | 9 | def map_array_to_range(arr: np.ndarray, mnv: float, mxv: float): 10 | """! 11 | \brief maps the array range to given interval 12 | """ 13 | narr = arr / arr.max() 14 | return mnv + (mxv - mnv) * narr 15 | 16 | 17 | def map_image_to_range(img: np.ndarray, mnv: float, mxv: float): 18 | """! 19 | \brief map image to a given range 20 | """ 21 | nimg = img.copy() 22 | if img.ndim == 2: 23 | return map_array_to_range(nimg, mnv=mnv, mxv=mxv) 24 | if img.ndim == 3: 25 | for channel in range(img.shape[2]): 26 | nimg[:, :, channel] = map_array_to_range( 27 | nimg[:, :, channel], mnv=mnv, mxv=mxv 28 | ) 29 | return nimg 30 | else: 31 | raise ValueError("number of dimensions must be 2/3 for image") 32 | 33 | 34 | def normalize_image(img: np.ndarray): 35 | """! 36 | \brief normalize image 37 | """ 38 | return img.astype(np.float) / 255.0 39 | 40 | 41 | def save_image(image, fname: str): 42 | """! 43 | \brief save image to path 44 | 45 | \param fname save path 46 | """ 47 | mnv, mxv = image.min(), image.max() 48 | if mxv <= 1.0: 49 | img = map_image_to_range(image, mnv=0.0, mxv=255.0) 50 | elif mxv >= 255.0: 51 | img = map_image_to_range(image, mnv=0.0, mxv=255.0) 52 | else: 53 | img = image.copy() 54 | img = img.astype("uint8") 55 | im = Image.fromarray(img) 56 | im.save(fname) 57 | -------------------------------------------------------------------------------- /.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 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | # added by me 132 | **/data/** 133 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # image-denoising-tf 2 | 3 | Image denoising algorithms implemented in tensorflow 2 4 | 5 | # Deep Image Prior 6 | 7 | The [deep image prior](doi.org/10.1007/s11263-020-01303-4) algorithm is 8 | implemented in tensorflow 2. 9 | 10 | 11 | Command line usage: 12 | ```bash 13 | usage: 14 | python deepimprior.py ./data/im03.png --outpath ./data/outimages/ --outprefix out_denoised --verbose 1 --epochs 10000 --learning_rate 0.1 --save_model_path ./data/outmodels/model --period 20 15 | 16 | Denoise a given image using deep image prior algorithm Beware of the following 17 | issues before proceeding with the usage of this script: - Convolution based 18 | algorithms are sensible to image size. Please use a square image. Ex: 800x800, 19 | or 600x600. - Image size significantly effects the training. Either make sure 20 | you have enough computation power, or adjust the image size appropriately. - 21 | Lastly as with all the gradient based methods we are using a stable learning 22 | rate. Feel free to adjust it before the training phase. I am thinking of 23 | adding decay learning rate option in the future. 24 | 25 | . 26 | positional arguments: 27 | imagepath path to the image 28 | 29 | optional arguments: 30 | -h, --help show this help message and exit 31 | --outpath OUTPATH path for saving outputs 32 | --outprefix OUTPREFIX 33 | prefix that will e prepended to intermediate files 34 | --epochs EPOCHS number of training epochs 35 | --verbose {0,1} verbose output during training 36 | --learning_rate LEARNING_RATE 37 | learning rate for the optimizer 38 | --save_model_path SAVE_MODEL_PATH 39 | Save model to path at each period of epochs 40 | --load_model_path LOAD_MODEL_PATH 41 | Load model from path to resume training 42 | --period PERIOD Periodic activity number, saving images, models etc at the end of each 43 | period/epoch number 44 | ``` 45 | 46 | Several use cases are implemented: 47 | 48 | - If you want to reuse the object that encapsulates the options covered in the 49 | command line usage in another setting just import the `DeepImPriorManager` 50 | object. Here is how to do it: 51 | 52 | ```python 53 | from deepimprior import DeepImPriorManager 54 | from PIL import Image 55 | 56 | verbose = True 57 | image_path = "./data/my_noisy_image.png" 58 | period = 200 # interval of epochs, used for scheduling callbacks 59 | learning_rate = 0.01 # the value is taken from the paper 60 | epochs = 2400 # the value is taken from the paper 61 | out_folder = "./data/outimages" 62 | out_prefix = "my_denoised_" 63 | save_model_path = "./data/outmodels/model" # save model here at the end of a period 64 | 65 | # if you want to resum training 66 | load_model_path: Optional[str] = "./data/outmodels/model_1000" 67 | 68 | # in verbose output you can save the model plot and its summary to a file 69 | plot_path = "plot_model.png", 70 | summary_path = "model_summary.txt", 71 | 72 | deep_prior = DeepImPriorManager( 73 | noisy_image = Image.open(image_path), 74 | verbose = verbose, 75 | period = period, 76 | learning_rate = learning_rate, 77 | epochs = epochs, 78 | out_folder = out_folder, 79 | out_prefix = out_prefix, 80 | save_model_path = save_model_path, 81 | load_model_path = load_model_path, 82 | plot_path = plot_path, 83 | summary_path = summary_path) 84 | 85 | # fits the model and saves it to "out_folder/outp_prefix-result.png" 86 | deep_prior.run_save() 87 | 88 | # or you can just fit the model and do something else with it like 89 | # evaluation etc. 90 | deep_prior.run() 91 | model = deep_prior.model 92 | 93 | # do other stuff with the model 94 | ``` 95 | 96 | If you want rebuild the architecture used in the paper, the `u_i`, `d_i` and 97 | `s_i` functions implement the components of the architecture implied in the 98 | figure 21 from page 19. They output a list of layers. No input shape is given 99 | during their creation. We diverge from the paper in padding. The paper uses 100 | reflect padding. Since the keras api does not implement reflect padding, we 101 | use the "same" padding option instead. 102 | 103 | For evaluation of list of layers that result from the above mentioned 104 | functions, we had written `apply_layers` function. The function is very 105 | simple: 106 | 107 | ```python 108 | def apply_layers(inlayer, lst: List[tf.keras.layers.Layer]): 109 | """! 110 | \brief apply layers consecutively 111 | \param inlayer input either a result of a previous evaluation or 112 | tf.keras.layers.Input. Notice that it is not tf.keras.layers.InputLayer 113 | """ 114 | x = inlayer 115 | for layer in lst: 116 | x = layer(x) 117 | return x 118 | ``` 119 | 120 | Here is the plot of the model: 121 | ![plot-of-model](./images/deep-image-prior-model.png) 122 | -------------------------------------------------------------------------------- /imdenoising/utils/layerutils.py: -------------------------------------------------------------------------------- 1 | """! 2 | \file layerutils.py functions for creation of layers 3 | """ 4 | 5 | 6 | import tensorflow as tf 7 | from typing import Optional, Tuple, List 8 | 9 | DATA_FORMAT = "channels_last" 10 | 11 | 12 | def check_initializer(init: str): 13 | """! 14 | """ 15 | cond1 = init == "glorot_uniform" 16 | cond2 = init == "glorot_normal" 17 | cond3 = init == "constant" 18 | cond4 = init == "he_normal" 19 | cond5 = init == "he_uniform" 20 | cond6 = init == "identity" 21 | cond7 = init == "lecun_normal" 22 | cond8 = init == "lecun_uniform" 23 | cond9 = init == "ones" 24 | cond10 = init == "orthogonal" 25 | cond11 = init == "random_normal" 26 | cond12 = init == "random_uniform" 27 | cond13 = init == "truncated_normal" 28 | cond14 = init == "variance_scaling" 29 | cond15 = init == "zeros" 30 | res = cond1 or cond2 or cond3 or cond4 or cond5 or cond6 or cond7 31 | res = res or cond8 or cond9 or cond10 or cond11 or cond12 or cond13 32 | res = res or cond14 or cond15 33 | return res 34 | 35 | 36 | def in2d( 37 | nb_rows: int, 38 | nb_cols: int, 39 | nb_channels: int, 40 | batch_size: int = 1, 41 | dtype=tf.float32, 42 | name: Optional[str] = None, 43 | ): 44 | """! 45 | \brief Create an input layer to be consumed by the model 46 | 47 | \return tf.keras.layers.Input input layer of the model 48 | """ 49 | if nb_rows < 1 or nb_cols < 1 or nb_channels < 1 or batch_size < 1: 50 | raise ValueError("input shape values must be bigger than 1") 51 | kwargs = { 52 | "shape": (int(nb_rows), int(nb_cols), int(nb_channels)), 53 | "dtype": dtype, 54 | "batch_size": batch_size, 55 | } 56 | if name is not None: 57 | return tf.keras.layers.Input(name=name, **kwargs) 58 | else: 59 | return tf.keras.layers.Input(**kwargs) 60 | 61 | 62 | # 63 | def lerelu(alpha=0.3, name: Optional[str] = None): 64 | """! 65 | \brief create a leaky relu layer 66 | \param alpha negative slope coefficient used in 67 | \f[f(x) = \alpha * x if x < 0 \f] 68 | """ 69 | if alpha == 1: 70 | raise ValueError("having 1 as value beats the purpose of using leaky relu") 71 | kwargs = {"alpha": float(alpha)} 72 | if name is not None: 73 | return tf.keras.layers.LeakyReLU(name=name, **kwargs) 74 | else: 75 | return tf.keras.layers.LeakyReLU(**kwargs) 76 | 77 | 78 | # 79 | def up2d( 80 | size_x: int = 2, 81 | size_y: int = 2, 82 | interpolation: str = "bilinear", 83 | name: Optional[str] = None, 84 | ): 85 | """! 86 | \brief create an upsampling layer 87 | 88 | \param size_x upsampling width size 89 | \param size_y upsampling height size 90 | \param interpolation interpolation method used in upsampling 91 | 92 | \return tf.Tensor with the shape (batch size, upsampled row, upsampled 93 | columns, channels) 94 | 95 | The layer expects that the following data format: 96 | (batch size, rows, cols, channels) 97 | """ 98 | if size_x <= 0 or size_y <= 0: 99 | raise ValueError("size can not be lower than 1") 100 | if interpolation != "bilinear" and interpolation != "nearest": 101 | raise ValueError("interpolation must be bilinear or nearest") 102 | size = (int(size_x), int(size_y)) 103 | kwargs = {"size": size, "data_format": DATA_FORMAT, "interpolation": interpolation} 104 | if name is not None: 105 | return tf.keras.layers.UpSampling2D(name=name, **kwargs) 106 | else: 107 | return tf.keras.layers.UpSampling2D(**kwargs) 108 | 109 | 110 | # 111 | def conv2d( 112 | nb_filter: int, 113 | ksize_x: int, 114 | ksize_y: int, 115 | stride: int, 116 | padding: str = "same", 117 | has_bias: bool = False, 118 | kernel_init: str = "glorot_uniform", 119 | bias_init: str = "glorot_uniform", 120 | name: Optional[str] = None, 121 | ): 122 | """! 123 | \brief create a 2d convolution layer 124 | 125 | pht: padding height top 126 | phb: padding height bottom 127 | pwt: padding width right 128 | pwb: padding width left 129 | 130 | sh: stride height 131 | ih: input height 132 | kh: kernel height 133 | sw: stride width 134 | iw: input width 135 | kw: kernel width 136 | 137 | Output height = (ih + pht + phb - kh) / (sh) + 1 138 | Output width = (iw + pwr + pwl - kw) / (sw) + 1 139 | 140 | output shape (batch size, output width, output height, filters) 141 | 142 | """ 143 | if padding != "same" and padding != "valid": 144 | raise ValueError("padding must be same or valid: " + padding) 145 | if ksize_x % 2 != 1 and ksize_y % 2 != 1: 146 | raise ValueError("kernel size values must be an odd number") 147 | if check_initializer(kernel_init) is False: 148 | raise ValueError("unknown kernel initializer") 149 | if check_initializer(bias_init) is False: 150 | raise ValueError("unknown bias initializer") 151 | # 152 | ksize = (int(ksize_x), int(ksize_y)) 153 | kwargs = { 154 | "filters": nb_filter, 155 | "kernel_size": ksize, 156 | "strides": stride, 157 | "padding": padding, 158 | "data_format": DATA_FORMAT, 159 | "use_bias": has_bias, 160 | "kernel_initializer": kernel_init, 161 | "bias_initializer": bias_init, 162 | } 163 | # 164 | if name is not None: 165 | return tf.keras.layers.Conv2D(name=name, **kwargs) 166 | else: 167 | return tf.keras.layers.Conv2D(**kwargs) 168 | 169 | 170 | # 171 | class ReflectPadding2D(tf.keras.layers.Layer): 172 | """! 173 | \brief reflect padding layer 174 | """ 175 | 176 | def __init__(self, padding=(1, 1), **kwargs): 177 | super(ReflectPadding2D, self).__init__(**kwargs) 178 | self.padding = tuple(padding) 179 | 180 | def call(self, arg): 181 | w_pad, h_pad = self.padding 182 | return tf.pad(arg, [[0, 0], [h_pad, h_pad], [w_pad, w_pad], [0, 0]], "REFLECT") 183 | 184 | 185 | # 186 | def batch_norm_layer(epsilon=0.001, momentum=0.99, gamma=1.0, **kwargs): 187 | """! 188 | \brief batch normalization layer 189 | """ 190 | axis = -1 if DATA_FORMAT == "channels_last" else 1 191 | return tf.keras.layers.BatchNormalization( 192 | axis=axis, 193 | momentum=momentum, 194 | epsilon=epsilon, 195 | gamma_initializer="ones", 196 | scale=True, 197 | **kwargs 198 | ) 199 | -------------------------------------------------------------------------------- /imdenoising/deepimprior.py: -------------------------------------------------------------------------------- 1 | """! 2 | \file deepimprior.py Deep Image prior implementation 3 | 4 | This file implements the deep image prior paper in tensorflow: 5 | 6 | Ulyanov, D., Vedaldi, A., Lempitsky, V., 2020. Deep Image Prior. Int J Comput Vis 128, 1867–1888. https://doi.org/10.1007/s11263-020-01303-4 7 | 8 | 9 | x = f_{theta}(z) 10 | 11 | x: image 12 | z: code vector ? -> random tensor of the type: \f[ R^{C \times W \times H}\f] 13 | z is a randomly initialized 3D tensor, [0, 0.1] 14 | theta: parameters: weights and bias of the filters in the network 15 | 16 | The neural network is interpreted as a parametrization of the function 17 | f_{theta}(z). By parametrization we mean that, by differentiating the function 18 | we can drive it to target output. 19 | 20 | f_{theta}: is a neural network. 21 | 22 | Without the additional data, the network captures the following statistics 23 | about the image x: 24 | 25 | - local, translation invariant convolutions 26 | - pixel neighborhood at multiple scales. 27 | 28 | Now we define an image denoising problem as the following: \f[p(x|x_0)\f]. 29 | Here the x_0 is the noisy image and we try to obtain the original image from 30 | it. 31 | Rather than modeling the distribution explicitly we regard as an optimization 32 | problem of the following type: 33 | \f[x' = argmin_x(E(x; x_0) + R(x)\f] 34 | 35 | - \f[R(x)\f]: regularizer term 36 | - x_0 is the low resolution/noisy occluded image 37 | - E(x; x_0) is data term: L^2 norm, that is \f[x' - x_0\f] where x' is the 38 | generated image and the x_0 is the original noisy image 39 | 40 | So minimizing data term, E(x;x_0) means minimizing mean squared error loss 41 | 42 | \f[ 43 | theta' = argmin_{theta} E(f_{theta}(z); x_0 ) 44 | x' = f_{theta'}(z) 45 | \f] 46 | 47 | - theta': local minimizer obtained using an optimizer such as gradient descent, 48 | adam, etc. 49 | - x_0: noisy image 50 | 51 | The hyper parameters provided by the paper: 52 | 53 | \f[z \in R^{3 \times W \times H} ∼ U(0, \frac{1}{10})\f] 54 | \f[n_u = n_d = [8, 16, 32, 64, 128]\f] 55 | \f[k_u = k_d = [3, 3, 3, 3, 3]\f] 56 | \f[n_s = [0, 0, 0, 4, 4]\f] 57 | \f[k_s = [NA, NA, NA, 1, 1]\f] 58 | \f[\sigma_p = 30\f] 59 | \f[num_iter = 2400\f] 60 | \f[LR = 0.01\f] 61 | \f[upsampling = bilinear\f] 62 | """ 63 | from typing import List, Optional 64 | from PIL import Image 65 | import tensorflow as tf 66 | import numpy as np 67 | import argparse 68 | import os 69 | 70 | 71 | from utils.tensorutils import random_tensor 72 | from utils.imutils import normalize_image 73 | from utils.layerutils import conv2d, in2d, lerelu, up2d, ReflectPadding2D 74 | from utils.layerutils import batch_norm_layer, in2d 75 | from utils.imutils import save_image 76 | from utils.imutils import map_array_to_range 77 | 78 | 79 | def make_z(ishape: List[int]): 80 | """! 81 | make z from paper 82 | """ 83 | if any([a < 1 for a in ishape]): 84 | raise ValueError("input shape can not have an element less than 1") 85 | arr = np.random.rand(*ishape) 86 | narr = map_array_to_range(arr, mnv=0.0, mxv=0.1) 87 | narr = np.expand_dims(narr, 0) 88 | return narr 89 | 90 | 91 | # 92 | def d_i(filter_downsampling: int, kernel_size_downsampling: int, index: int = 1): 93 | """! 94 | \brief reproducing d_i from page 19 figure 21 of article 95 | """ 96 | kd = kernel_size_downsampling 97 | conv_2 = conv2d( 98 | nb_filter=filter_downsampling, 99 | ksize_x=kd, 100 | ksize_y=kd, 101 | stride=2, 102 | name="d_i_conv2d_" + str(filter_downsampling) + "_" + str(index), 103 | padding="same", 104 | ) 105 | # output rows = (Input height + 0 + 0 - kernel height) / (stride height) + 1 106 | conv_1 = conv2d( 107 | nb_filter=filter_downsampling, ksize_x=kd, ksize_y=kd, stride=1, padding="same" 108 | ) 109 | bn1 = batch_norm_layer(gamma=0.99) 110 | bn2 = batch_norm_layer(gamma=0.99) 111 | # 112 | return [ 113 | # first 114 | conv_2, 115 | # ReflectPadding2D(padding=(2, 2)), 116 | bn1, 117 | lerelu(), 118 | # second 119 | conv_1, 120 | # ReflectPadding2D(padding=(1, 1)), 121 | bn2, 122 | lerelu(), 123 | ] 124 | 125 | 126 | # 127 | def s_i(filter_skip: int, kernel_size_skip: int, index: int = 1): 128 | """! 129 | \brief reproducing s_i from page 19 figure 21 of article 130 | """ 131 | ns = filter_skip 132 | ks = kernel_size_skip 133 | conv_1 = conv2d( 134 | nb_filter=ns, 135 | ksize_x=ks, 136 | ksize_y=ks, 137 | stride=1, 138 | padding="same", 139 | name="s_i_conv2d_" + str(filter_skip) + "_" + str(index), 140 | ) 141 | bn = batch_norm_layer(gamma=0.99) 142 | return [conv_1, bn, lerelu()] # ReflectPadding2D(padding=(1, 1)), 143 | 144 | 145 | # 146 | def u_i(filter_upsampling: int, kernel_size_upsampling: int, index: int = 1): 147 | """! 148 | \brief reproducing u_i from page 19 figure 21 of article 149 | """ 150 | nu = filter_upsampling 151 | ku = kernel_size_upsampling 152 | return [ 153 | # first 154 | conv2d( 155 | nb_filter=nu, 156 | ksize_x=ku, 157 | ksize_y=ku, 158 | stride=1, 159 | padding="same", 160 | name="u_i_conv2d_" + str(nu) + "_" + str(index), 161 | ), 162 | # ReflectPadding2D(padding=(1, 1)), 163 | batch_norm_layer(gamma=0.99), 164 | lerelu(), 165 | # second 166 | conv2d(nb_filter=nu, ksize_x=ku, ksize_y=ku, stride=1, padding="same"), 167 | # ReflectPadding2D(), 168 | batch_norm_layer(gamma=0.99), 169 | lerelu(), 170 | # up sample 171 | up2d(size_x=2, size_y=2, interpolation="bilinear"), 172 | ] 173 | 174 | 175 | # 176 | def apply_layers(inlayer, lst: List[tf.keras.layers.Layer]): 177 | """! 178 | \brief apply layers consecutively 179 | """ 180 | x = inlayer 181 | for layer in lst: 182 | x = layer(x) 183 | return x 184 | 185 | 186 | class DeepImPriorTrainModel(tf.keras.Model): 187 | """! 188 | \brief custom training model for fine tuning training process 189 | """ 190 | 191 | def __init__(self, **kwargs): 192 | super().__init__(**kwargs) 193 | 194 | self.predicted_value = None 195 | 196 | def train_step(self, data): 197 | """! 198 | \brief training step with additive noise as per paper 199 | 200 | standard deviation value is taken from paper page 19 201 | """ 202 | # 203 | 204 | if len(data) == 3: 205 | z, y_img, sample_weight = data 206 | else: 207 | sample_weight = None 208 | z, y_img = data 209 | # 210 | zshape = z.shape.as_list() 211 | for i in range(len(zshape)): 212 | if zshape[i] is None: 213 | zshape[i] = 1 214 | mean = 0 215 | sigma = 1.0 / 30 216 | noise = np.random.default_rng().normal(mean, sigma, size=zshape) 217 | z += noise 218 | 219 | # 220 | with tf.GradientTape() as tape: 221 | y_pred = self(z, training=True) 222 | # reshape the prediction to match the z 223 | zs = [s for s in zshape] 224 | zs[-1] = -1 225 | y_prediction = tf.reshape(y_pred, zs) 226 | 227 | self.predicted_value = (y_prediction, y_img, z) 228 | # y_prediction = y_pred 229 | loss = self.compiled_loss( 230 | y_img, 231 | y_prediction, 232 | sample_weight=sample_weight, 233 | regularization_losses=self.losses, 234 | ) 235 | # save prediction as ndarray to save it later on 236 | # Compute gradients 237 | trainable_vars = self.trainable_variables 238 | gradients = tape.gradient(loss, trainable_vars) 239 | # Update weights 240 | self.optimizer.apply_gradients(zip(gradients, trainable_vars)) 241 | # Update metrics (includes the metric that tracks the loss) 242 | self.compiled_metrics.update_state(y_img, y_prediction) 243 | # Return a dict mapping metric names to current value 244 | return {m.name: m.result() for m in self.metrics} 245 | 246 | 247 | # 248 | class DeepImPriorImSaveCallback(tf.keras.callbacks.Callback): 249 | """! 250 | \brief Save image depending on the epoch 251 | """ 252 | 253 | def __init__( 254 | self, 255 | impath: str, 256 | mpath: str, 257 | imshape: List[int], 258 | verbose_save: bool, 259 | period: int, 260 | **kwargs 261 | ): 262 | super().__init__(**kwargs) 263 | self.impath = impath 264 | self.imshape = imshape 265 | self.model_path = mpath 266 | self.verbose_save = verbose_save 267 | self.period = period 268 | 269 | def save_model_to_path(self, epoch: int): 270 | """! 271 | save model to given path 272 | """ 273 | mpath = self.model_path + "_" + str(epoch) 274 | tf.keras.models.save_model( 275 | self.model, mpath, overwrite=True, include_optimizer=True, save_traces=True 276 | ) 277 | 278 | def on_epoch_end(self, epoch, logs=None): 279 | """! 280 | On total there should be 2400 epoch as per paper page 19 281 | """ 282 | if epoch % self.period == 0: 283 | pred, orig, noise = self.model.predicted_value 284 | im = pred.numpy().reshape(*self.imshape) 285 | # save prediction 286 | imname = self.impath + "_" + str(epoch) + ".png" 287 | save_image(image=im, fname=imname) 288 | if self.model_path is not None: 289 | self.save_model_to_path(epoch=epoch) 290 | if self.verbose_save: 291 | orig = orig.numpy().reshape(*self.imshape) 292 | noise = noise.numpy().reshape(*self.imshape) 293 | # save original 294 | imname = self.impath + "_orig_" + str(epoch) + ".png" 295 | save_image(image=orig, fname=imname) 296 | # save noise 297 | imname = self.impath + "_noise_" + str(epoch) + ".png" 298 | save_image(image=noise, fname=imname) 299 | 300 | 301 | class DeepImPriorManager: 302 | "" 303 | 304 | def __init__( 305 | self, 306 | noisy_image: Image, 307 | verbose: bool, 308 | period: int, 309 | learning_rate: float, 310 | epochs: int, 311 | out_folder: str, 312 | out_prefix: str, 313 | save_model_path: Optional[str] = None, 314 | load_model_path: Optional[str] = None, 315 | plot_path: str = "plot_model.png", 316 | summary_path: str = "model_summary.txt", 317 | optimizer: str = "adam", 318 | ): 319 | """! 320 | Deep Image Prior training manager 321 | """ 322 | self.image_info = ( 323 | noisy_image.height, 324 | noisy_image.width, 325 | len(noisy_image.split()), 326 | ) 327 | self.noisy_imarr = np.array(noisy_image) 328 | # 329 | self.verbose = verbose 330 | self.period = period 331 | # 332 | self.learning_rate = learning_rate 333 | self.epochs = epochs 334 | # 335 | self.out_impath = os.path.join(out_folder, out_prefix) 336 | # 337 | self.save_model_path = save_model_path 338 | self.load_model_path = load_model_path 339 | # 340 | self.plot_path = os.path.join(out_folder, plot_path) 341 | self.summary_path = os.path.join(out_folder, summary_path) 342 | # optimizer 343 | self.optimizer = optimizer 344 | self._cback = None 345 | self._model = None 346 | 347 | @property 348 | def callback(self) -> DeepImPriorImSaveCallback: 349 | """! 350 | Prepare the DeepImPriorImSaveCallback 351 | """ 352 | if self._cback is None: 353 | self._cback = DeepImPriorImSaveCallback( 354 | impath=self.out_impath, 355 | imshape=self.image_info, 356 | mpath=self.save_model_path, 357 | verbose_save=self.verbose, 358 | period=self.period, 359 | ) 360 | return self._cback 361 | 362 | @property 363 | def model(self) -> DeepImPriorTrainModel: 364 | """! 365 | \brief create the model that follows the architecture of the paper 366 | """ 367 | if self._model is None: 368 | self._model = self.prep_model() 369 | return self._model 370 | 371 | def prep_model(self) -> DeepImPriorTrainModel: 372 | """! 373 | \brief create the model that follows the architecture of the paper 374 | 375 | The model is taken from page 19 figure 21 376 | """ 377 | # 378 | rows, cols, channels = self.image_info 379 | # input layer 380 | ilayer = in2d(nb_rows=rows, nb_cols=cols, nb_channels=channels) 381 | # 382 | # down sampling layer 383 | skips = [] 384 | k_d_s = 3 385 | x = ilayer 386 | n_ds = [8, 16, 32, 64, 128] 387 | for i in range(len(n_ds)): 388 | n_d = n_ds[i] 389 | lst = d_i(filter_downsampling=n_d, kernel_size_downsampling=k_d_s, index=i) 390 | x = apply_layers(x, lst) 391 | skips.append(x) 392 | # 393 | # upsampling with skip connections 394 | # n_us = list(reversed([8, 16, 32, 64, 128])) 395 | n_us = [8, 16, 32, 64, 128] 396 | n_ss = [0, 0, 0, 4, 4] 397 | k_ss = [None, None, None, 1, 1] 398 | for i in range(len(n_us)): 399 | # 400 | k_s = k_ss[i] 401 | n_u = n_us[i] 402 | n_s = n_ss[i] 403 | # 404 | if k_s is not None: 405 | x_ = skips[i] 406 | lst_s = s_i(filter_skip=n_s, kernel_size_skip=k_s, index=i) 407 | x_ = apply_layers(x_, lst_s) 408 | 409 | # resize the upsampled tensor to the skip connection tensor 410 | # except for the concatenation axis which is the last axis 411 | shapelst = list(x_.shape[:-1]) 412 | shapelst.append(-1) 413 | x1_ = tf.reshape(x, shape=shapelst) 414 | 415 | x = tf.keras.layers.Concatenate()([x1_, x_]) 416 | # 417 | lst_u = u_i( 418 | filter_upsampling=n_u, kernel_size_upsampling=k_d_s, index=i 419 | ) 420 | x = apply_layers(x, lst_u) 421 | 422 | else: 423 | lst_u = u_i( 424 | filter_upsampling=n_u, kernel_size_upsampling=k_d_s, index=i 425 | ) 426 | x = apply_layers(x, lst_u) 427 | # 428 | # last 429 | # upsampling to match z shape 430 | last = up2d(size_x=4, size_y=2, interpolation="bilinear") 431 | x = last(x) 432 | return DeepImPriorTrainModel(inputs=ilayer, outputs=x) 433 | 434 | def choose_optimizer(self): 435 | "Choose an optimizer" 436 | optimizer = None 437 | if self.optimizer.lower() == "adam": 438 | optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate) 439 | elif self.optimizer.lower() == "adamax": 440 | optimizer = tf.keras.optimizers.Adamax(learning_rate=self.learning_rate) 441 | elif self.optimizer.lower() == "rmsprop": 442 | optimizer = tf.keras.optimizers.RMSprop(learning_rate=self.learning_rate) 443 | elif self.optimizer.lower() == "adadelta": 444 | optimizer = tf.keras.optimizers.Adadelta(learning_rate=self.learning_rate) 445 | elif self.optimizer.lower() == "adagrad": 446 | optimizer = tf.keras.optimizers.Adagrad(learning_rate=self.learning_rate) 447 | elif self.optimizer.lower() == "ftrl": 448 | optimizer = tf.keras.optimizers.Ftrl(learning_rate=self.learning_rate) 449 | elif self.optimizer.lower() == "nadam": 450 | optimizer = tf.keras.optimizers.Nadam(learning_rate=self.learning_rate) 451 | else: 452 | optimizer = tf.keras.optimizers.SGD(learning_rate=self.learning_rate) 453 | return optimizer 454 | 455 | def compile_model(self, optimizer_weights=None): 456 | """! 457 | Compile model with loss and optimization 458 | learning rate is taken from the page 19 459 | """ 460 | if optimizer_weights is None: 461 | optimizer = self.choose_optimizer() 462 | self.model.compile( 463 | optimizer=optimizer, 464 | loss=tf.keras.losses.MSE, 465 | metrics=["accuracy", "mae"], 466 | run_eagerly=True, 467 | ) 468 | else: 469 | optimizer, weights = optimizer_weights 470 | self.model.compile( 471 | optimizer=optimizer, 472 | loss=tf.keras.losses.MSE, 473 | metrics=["accuracy", "mae"], 474 | run_eagerly=True, 475 | ) 476 | self.model.set_weights(weights) 477 | 478 | def init_model(self, optimizer_weights=None): 479 | """! 480 | Initialize model 481 | """ 482 | # 483 | self.compile_model(optimizer_weights=optimizer_weights) 484 | # 485 | if self.verbose: 486 | with open(self.summary_path, "w", encoding="utf-8") as f: 487 | self.model.summary(print_fn=lambda x: f.write(x + "\n")) 488 | tf.keras.utils.plot_model( 489 | self.model, to_file=self.plot_path, show_dtype=True, show_shapes=True 490 | ) 491 | 492 | def fit_model(self, x_train: np.ndarray, y_train: np.ndarray): 493 | """! 494 | \brief fit model 495 | """ 496 | self.model.fit( 497 | x=x_train, 498 | y=y_train, 499 | epochs=self.epochs, 500 | verbose=self.verbose, 501 | callbacks=[self.callback], 502 | ) 503 | 504 | def predict_model(self, data: np.ndarray): 505 | """! 506 | """ 507 | pred = self.model.predict(data) 508 | im = pred.reshape(*self.noisy_imarr.shape) 509 | return im 510 | 511 | def save_image(self, image: np.ndarray): 512 | """! 513 | \brief Save image 514 | """ 515 | imname = self.callback.impath + "_" + "result" + ".png" 516 | save_image(image=image, fname=imname) 517 | 518 | def make_z_train(self): 519 | """! 520 | \brief make z vector 521 | """ 522 | return make_z(ishape=self.noisy_imarr.shape) 523 | 524 | def make_train_target(self): 525 | "" 526 | return self.noisy_imarr[np.newaxis, :] 527 | 528 | def run(self): 529 | """! 530 | \brief run model 531 | """ 532 | if self.load_model_path is not None: 533 | model = tf.keras.models.load_model(self.load_model_path) 534 | weights = model.get_weights() 535 | optimizer = model.optimizer 536 | self.init_model(optimizer_weights=(optimizer, weights)) 537 | else: 538 | self.init_model() 539 | # 540 | x_train = self.make_z_train() 541 | y_train = self.make_train_target() 542 | self.fit_model(x_train=x_train, y_train=y_train) 543 | 544 | def run_save(self): 545 | """! 546 | \brief run and save the model 547 | """ 548 | self.run() 549 | pred = self.predict_model(data=self.noisy_imarr.copy()) 550 | self.save_image(image=pred) 551 | 552 | 553 | # 554 | def make_parser(): 555 | """! 556 | create the argument parser and other related functions for io 557 | """ 558 | parser = argparse.ArgumentParser( 559 | description=""" 560 | Denoise a given image using deep image prior algorithm. 561 | 562 | Beware of the following issues before proceeding with the usage of this script: 563 | - Convolution based algorithms are sensible to image size. Please use a square 564 | image. Ex: 800x800, or 600x600. 565 | 566 | - Image size significantly effects the training. Either make sure you have 567 | enough computation power, or adjust the image size appropriately. 568 | 569 | - Lastly as with all the gradient based methods we are using a stable learning 570 | rate. Feel free to adjust it before the training phase. I am thinking of 571 | adding decay learning rate option in the future. 572 | """, 573 | usage=""" 574 | python deepimprior.py ./data/im03.png --outpath ./data/outimages/ --outprefix 575 | out_denoised --verbose 1 --epochs 10000 --learning_rate 0.1 --save_model_path 576 | ./data/outmodels/model --period 20 --optimizer adam 577 | """, 578 | ) 579 | parser.add_argument("imagepath", help="path to the image") 580 | parser.add_argument("--outpath", help="path for saving outputs", default="./") 581 | parser.add_argument( 582 | "--outprefix", 583 | help="prefix that will be prepended to intermediate files", 584 | default="outimg", 585 | ) 586 | parser.add_argument( 587 | "--epochs", help="number of training epochs", type=int, default=2400 588 | ) 589 | parser.add_argument( 590 | "--verbose", 591 | help="verbose output during training", 592 | type=int, 593 | default=0, 594 | choices=[0, 1], 595 | ) 596 | parser.add_argument( 597 | "--learning_rate", 598 | help="learning rate for the optimizer", 599 | type=float, 600 | default=0.01, 601 | ) 602 | 603 | parser.add_argument( 604 | "--save_model_path", 605 | help="Save model to path at each period of epochs", 606 | default=None, 607 | ) 608 | parser.add_argument( 609 | "--load_model_path", 610 | help="Load model from path to resume training", 611 | default=None, 612 | ) 613 | parser.add_argument( 614 | "--period", 615 | help="Periodic activity number, saving images, models etc at the end of each period/epoch number", 616 | type=int, 617 | default=200, 618 | ) 619 | parser.add_argument( 620 | "--optimizer", 621 | help="Optimizer to be used in the training process", 622 | type=str, 623 | choices=[ 624 | "adam", 625 | "adamax", 626 | "adadelta", 627 | "adagrad", 628 | "ftrl", 629 | "nadam", 630 | "rmsprop", 631 | "sgd", 632 | ], 633 | default="adam", 634 | ) 635 | return parser 636 | 637 | 638 | def main_fn(): 639 | "" 640 | parser = make_parser() 641 | args = parser.parse_args() 642 | verbose = bool(args.verbose) 643 | epochs = args.epochs 644 | if epochs <= 0: 645 | raise ValueError("epochs must be bigger than 0") 646 | # 647 | noisy_image = Image.open(args.imagepath) 648 | manager = DeepImPriorManager( 649 | noisy_image=noisy_image, 650 | verbose=verbose, 651 | epochs=args.epochs, 652 | learning_rate=args.learning_rate, 653 | period=args.period, 654 | out_folder=args.outpath, 655 | out_prefix=args.outprefix, 656 | save_model_path=args.save_model_path, 657 | load_model_path=args.load_model_path, 658 | optimizer=args.optimizer, 659 | ) 660 | manager.run_save() 661 | 662 | 663 | if __name__ == "__main__": 664 | main_fn() 665 | -------------------------------------------------------------------------------- /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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Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. 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"Knowingly relying" means you have 508 | actual knowledge that, but for the patent license, your conveying the 509 | covered work in a country, or your recipient's use of the covered work 510 | in a country, would infringe one or more identifiable patents in that 511 | country that you have reason to believe are valid. 512 | 513 | If, pursuant to or in connection with a single transaction or 514 | arrangement, you convey, or propagate by procuring conveyance of, a 515 | covered work, and grant a patent license to some of the parties 516 | receiving the covered work authorizing them to use, propagate, modify 517 | or convey a specific copy of the covered work, then the patent license 518 | you grant is automatically extended to all recipients of the covered 519 | work and works based on it. 520 | 521 | A patent license is "discriminatory" if it does not include within 522 | the scope of its coverage, prohibits the exercise of, or is 523 | conditioned on the non-exercise of one or more of the rights that are 524 | specifically granted under this License. You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. 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 | 635 | Copyright (C) 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 | 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 | --------------------------------------------------------------------------------