├── README-zh.md ├── README.md ├── .gitignore ├── project_test_script.py ├── datasets_prepare.py ├── dataloader.py ├── net_model.py └── LICENSE /README-zh.md: -------------------------------------------------------------------------------- 1 | # DeHaze_MSCNN 2 | Single Image Dehazing via Multi-scale Convolutional Neural Networks论文去雾代码 3 | 4 | - [English Version](https://github.com/raven-dehaze-work/MSCNN_Keras/blob/master/README.md) 5 | - [Chinese Version](https://github.com/raven-dehaze-work/MSCNN_Keras/blob/master/README-zh.md) 6 | 7 | 8 | ### 1. 网络结构 9 | 10 | ![](https://ae01.alicdn.com/kf/HTB1atEZc21G3KVjSZFkq6yK4XXaw.jpg) 11 | 12 | ## 2. 去雾算法去雾步骤 13 | 14 | ![](https://ae01.alicdn.com/kf/HTB1ELZWc3aH3KVjSZFjq6AFWpXaa.jpg) 15 | 16 | ### 3. 其他去雾算法 17 | 18 | 1. [DCP去雾Matlab实现](https://github.com/raven-dehaze-work/DCP-Dehaze) 19 | 2. [DeblurGanToDehaze](https://github.com/raven-dehaze-work/DeblurGanToDehaze) 20 | 3. [MSCNN去雾Keras实现](https://github.com/raven-dehaze-work/MSCNN_Keras) 21 | 4. [MSCNN去雾TensorFlow实现](https://github.com/dishank-b/MSCNN-Dehazing-Tensorflow) 22 | 5. [Dehaze-GAN TensorFlow实现](https://github.com/raven-dehaze-work/Dehaze-GAN) 23 | 24 | 25 | 26 | 对应博文:https://www.ravenxrz.ink/archives/summary-3-paper-reproduction-2-mscnn-defogging-1.html -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DeHaze_MSCNN 2 | The implemention code of Single Image Dehazing via Multi-scale Convolutional Neural Networks . 3 | 4 | - [English Version](https://github.com/raven-dehaze-work/MSCNN_Keras/blob/master/README.md) 5 | - [Chinese Version](https://github.com/raven-dehaze-work/MSCNN_Keras/blob/master/README-zh.md) 6 | 7 | ### 1. Network structure 8 | 9 | ![](https://ae01.alicdn.com/kf/HTB1atEZc21G3KVjSZFkq6yK4XXaw.jpg) 10 | 11 | ## 2. Main steps of the proposed single-image dehazing algorithm 12 | 13 | ![](https://ae01.alicdn.com/kf/HTB1ELZWc3aH3KVjSZFjq6AFWpXaa.jpg) 14 | 15 | ### 3. Other defogging algorithms 16 | 17 | 1. [DCP去雾Matlab实现](https://github.com/raven-dehaze-work/DCP-Dehaze) 18 | 2. [DeblurGanToDehaze](https://github.com/raven-dehaze-work/DeblurGanToDehaze) 19 | 3. [MSCNN去雾Keras实现](https://github.com/raven-dehaze-work/MSCNN_Keras) 20 | 4. [MSCNN去雾TensorFlow实现](https://github.com/dishank-b/MSCNN-Dehazing-Tensorflow) 21 | 5. [Dehaze-GAN TensorFlow实现](https://github.com/raven-dehaze-work/Dehaze-GAN) 22 | 23 | Corresponding blog post:https://www.ravenxrz.ink/archives/summary-3-paper-reproduction-2-mscnn-defogging-1.html 24 | -------------------------------------------------------------------------------- /.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 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | 106 | # user define 107 | .idea 108 | test.py 109 | datasets/ 110 | dehazed_result/ 111 | reside_datasets/ 112 | model_save - 副本/ 113 | -------------------------------------------------------------------------------- /project_test_script.py: -------------------------------------------------------------------------------- 1 | """ 2 | 辅助脚本，用于测试一些编程中的功能，不重要 3 | """ 4 | 5 | from random import choice 6 | import glob 7 | import os 8 | import numpy as np 9 | 10 | 11 | img_dir = r'D:\Projects\PythonProjects\MSCNN_Keras\train_datasets_img' 12 | npy_dir = r'D:\Projects\PythonProjects\MSCNN_Keras\train_datasets_npy' 13 | 14 | # 读取clear目录下的所有文件名 15 | clear_file_names = glob.glob(os.path.join(img_dir,'clear/*')) 16 | # 去除所有后缀 17 | clear_file_names = list(map(lambda x:os.path.splitext( 18 | os.path.basename(x) 19 | )[0],clear_file_names)) 20 | 21 | # 循环去除多余文件 22 | for file_name in clear_file_names: 23 | # 寻找trans匹配对 24 | trans_file_names = glob.glob(os.path.join(img_dir,'trans',file_name+"_*")) 25 | # 随机选择一个要剩下的 26 | trans_remained_file = choice(trans_file_names) 27 | 28 | # 删除其余所有不需要的 29 | for file in trans_file_names: 30 | if trans_remained_file != file: 31 | print(file) 32 | os.remove(file) 33 | 34 | # 删除haze file 35 | remained_prefix = os.path.splitext(os.path.basename(trans_remained_file))[0] 36 | # 寻找haze匹配对 37 | haze_file_names = glob.glob(os.path.join(img_dir,'haze',file_name+"_*")) 38 | # 用remained_prefix 去匹配 寻找到的haze_file匹配对，所有不匹配的全部删除 39 | for file in haze_file_names: 40 | if not remained_prefix + "_" in file: 41 | print(file) 42 | os.remove(file) 43 | 44 | # 删除npy对应的文件 45 | # 删除haze 46 | npy_haze_file_names = glob.glob(os.path.join(npy_dir,'haze',file_name+"_*")) 47 | # 删除所有不需要的 48 | for file in npy_haze_file_names: 49 | if not remained_prefix+"_" in file: 50 | print(file) 51 | os.remove(file) 52 | 53 | # 删除trans 54 | npy_trans_file_names = glob.glob(os.path.join(npy_dir, 'trans', file_name + "_*")) 55 | # 删除所有不需要的 56 | for file in npy_trans_file_names: 57 | if not remained_prefix+"." in file: 58 | print(file) 59 | os.remove(file) -------------------------------------------------------------------------------- /datasets_prepare.py: -------------------------------------------------------------------------------- 1 | """ 2 | 数据集准备脚本，实现功能 3 | 1. 从ITS文件夹中读取相应数据集 4 | 2. 将所有图片转为320*240的大小并存为npy文件 5 | """ 6 | import glob 7 | import numpy as np 8 | import os 9 | from PIL import Image 10 | 11 | 12 | # 定义目录参数 13 | # 原始数据集合 14 | clear_img_dir = r'D:\Projects\Dehaze\数据集合\ITS\clear' 15 | hazy_img_dir = r'D:\Projects\Dehaze\数据集合\ITS\hazy' 16 | trans_img_dir = r'D:\Projects\Dehaze\数据集合\ITS\trans' 17 | 18 | # 将原始图片放缩后存放的图片目录 19 | clear_scaled_dir = r'D:\Projects\PythonProjects\MSCNN_Keras\train_datasets_img\clear' 20 | hazy_scaled_dir = r'D:\Projects\PythonProjects\MSCNN_Keras\train_datasets_img\haze' 21 | trans_scaled_dir = r'D:\Projects\PythonProjects\MSCNN_Keras\train_datasets_img\trans' 22 | 23 | # 将原始图片放缩后的npy文件存放目录 24 | clear_npy_dir = r'D:\Projects\PythonProjects\MSCNN_Keras\train_datasets_npy\clear' 25 | hazy_npy_dir = r'D:\Projects\PythonProjects\MSCNN_Keras\train_datasets_npy\haze' 26 | trans_npy_dir = r'D:\Projects\PythonProjects\MSCNN_Keras\train_datasets_npy\trans' 27 | 28 | # 定义缩放后的图片大小 29 | scaled_img_width = 320 30 | scaled_img_height = 240 31 | 32 | 33 | def convert_img2npy(img_dir, npy_dir,save_scaled=False,scaled_dir=None): 34 | """ 35 | 将图片缩放并转换为npy文件，方便后续训练使用 仅转换.png后缀 36 | :param img_dir: 图片目录 37 | :param npy_dir: 保存的npy文件目录 38 | :param save_scaled: 是否需要保存缩放后的图片 39 | :param scaled_dir: 缩放后保存的目录 40 | :return: 41 | """ 42 | imgs_path = glob.glob(os.path.join(img_dir, "*.png")) 43 | 44 | for filepath in imgs_path: 45 | file_name = os.path.basename(filepath) 46 | print('convert %s' % file_name) 47 | 48 | # 打开图片 49 | img = Image.open(filepath) 50 | img = img.resize((scaled_img_width, scaled_img_height)) 51 | 52 | # 是否保存sacled的图片 53 | if save_scaled and scaled_dir: 54 | img.save(os.path.join(scaled_dir, file_name)) 55 | 56 | # 转为npy 57 | img_np = np.array(img) 58 | # 保存 59 | np.save(os.path.join(npy_dir, file_name.replace("png", "npy")), img_np) 60 | 61 | 62 | if __name__ == '__main__': 63 | convert_img2npy(clear_img_dir,clear_npy_dir,True,clear_scaled_dir) 64 | convert_img2npy(hazy_img_dir,hazy_npy_dir,True,hazy_scaled_dir) 65 | convert_img2npy(trans_img_dir,trans_npy_dir,True,trans_scaled_dir) 66 | -------------------------------------------------------------------------------- /dataloader.py: -------------------------------------------------------------------------------- 1 | """ 2 | 数据加载脚本，用于辅助训练 3 | """ 4 | import os 5 | import glob 6 | import numpy as np 7 | 8 | 9 | class DataLoader: 10 | """ 11 | 数据加载类 12 | """ 13 | 14 | def __init__(self, batch_size=100) -> None: 15 | super().__init__() 16 | # 加载各数据生成器 17 | 18 | self.train_generator = self._build_data_generator(batch_size) 19 | self.train_size = len( 20 | glob.glob(os.path.join(r'D:\Projects\PythonProjects\MSCNN_Keras\train_datasets_npy\haze', '*'))) 21 | self.test_size = len( 22 | glob.glob(os.path.join(r'D:\Projects\PythonProjects\MSCNN_Keras\test_datasets_npy\haze', '*'))) 23 | 24 | def _build_data_generator(self, batch_size): 25 | # 加载所有文件名 26 | haze_file_names = glob.glob( 27 | os.path.join(r'D:\Projects\PythonProjects\MSCNN_Keras\train_datasets_npy\haze', '*')) 28 | trans_file_names = glob.glob( 29 | os.path.join(r'D:\Projects\PythonProjects\MSCNN_Keras\train_datasets_npy\trans', '*')) 30 | 31 | # 因为读入的haze 和 trans 文件目前还不是匹配的pair，所以做一下匹配处理 32 | pairs_num = len(haze_file_names) 33 | pairs = {'haze': ['' for i in range(pairs_num)], 34 | 'trans': ['' for i in range(pairs_num)]} 35 | 36 | # 匹配 37 | for idx, file_name in enumerate(trans_file_names): 38 | pairs['trans'][idx] = file_name 39 | prefix = os.path.splitext( 40 | os.path.basename(file_name) 41 | )[0] 42 | for haze_file_name in haze_file_names: 43 | if prefix + "_" in haze_file_name: 44 | pairs['haze'][idx] = haze_file_name 45 | haze_file_names.remove(haze_file_name) 46 | break 47 | 48 | x_datas = np.zeros((batch_size, 240, 320, 3)) 49 | y_datas = np.zeros((batch_size, 240, 320, 1)) 50 | pairs['haze'] = np.array(pairs['haze']) 51 | pairs['trans'] = np.array(pairs['trans']) 52 | # 产生生成器 53 | while True: 54 | # 随机扰乱 55 | permutated_indexes = np.random.permutation(pairs_num) 56 | if (pairs_num < batch_size): 57 | # 所有数据量不足以提供一个batch_size 58 | x_paths = pairs['haze'][permutated_indexes] 59 | y_paths = pairs['trans'][permutated_indexes] 60 | for idx in range(pairs_num): 61 | x_datas[idx] = np.load(x_paths[idx]) 62 | y_datas[idx] = np.load(y_paths[idx]) 63 | yield x_datas, y_datas 64 | else: 65 | # 数量能够提供 66 | for index in range(pairs_num // batch_size): 67 | batch_indexes = permutated_indexes[index * batch_size:(index + 1) * batch_size] 68 | 69 | # batch_pairs仅仅是些文件路径 70 | x_paths = pairs['haze'][batch_indexes] 71 | y_paths = pairs['trans'][batch_indexes] 72 | 73 | # 现在要把这些文件路径对应的npy文件读取成npy arr 74 | for idx in range(batch_size): 75 | x_datas[idx] = np.load(x_paths[idx]) 76 | y_datas[idx] = np.load(y_paths[idx]).reshape((240,320,1)) 77 | 78 | yield x_datas, y_datas 79 | 80 | def load_test_datasets(self, size): 81 | """ 82 | 加载测试数据集合。 83 | :param size 加载多少张测试图片，测试图片包括 haze图和trans图（后者用于对比） 84 | :return: haze,trans 元祖 85 | """ 86 | choose_idx = np.random.randint(0, self.test_size, size) 87 | trans_paths = glob.glob(os.path.join(r'D:\Projects\PythonProjects\MSCNN_Keras\test_datasets_npy\trans', '*')) 88 | haze_paths = glob.glob(os.path.join(r'D:\Projects\PythonProjects\MSCNN_Keras\test_datasets_npy\haze', '*')) 89 | trans = np.zeros((size, 240, 320, 1)) 90 | haze = np.zeros((size, 240, 320, 3)) 91 | 92 | for idx in range(size): 93 | trans[idx] = np.load(trans_paths[choose_idx[idx]]).reshape((240,320,1)) 94 | haze[idx] = np.load(haze_paths[choose_idx[idx]]) 95 | return haze, trans 96 | if __name__ == '__main__': 97 | DataLoader() -------------------------------------------------------------------------------- /net_model.py: -------------------------------------------------------------------------------- 1 | """ 2 | 网络模型搭建 3 | """ 4 | import os 5 | from keras import backend as K 6 | import tensorflow as tf 7 | from keras.engine.topology import Layer 8 | from keras.models import Model 9 | from keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Input, BatchNormalization 10 | from keras.layers.merge import concatenate 11 | 12 | 13 | def build_net_model(): 14 | """ 15 | 建立paper中的MSCNN 16 | :return: corseModel和fineModel的元祖 17 | """ 18 | img_height = 240 19 | img_width = 320 20 | channel = 3 21 | 22 | # 通用输入 23 | input_img = Input((img_height, img_width, channel)) 24 | coarseNet = _build_coarseNet(input_img) 25 | fineNet = _build_fineNet(input_img, coarseNet) 26 | 27 | # 建立coarse Model和fine Model 28 | coarseModel = Model(inputs=input_img, outputs=coarseNet) 29 | fineModel = Model(inputs=input_img, outputs=fineNet) 30 | 31 | # summary 32 | coarseModel.summary() 33 | fineModel.summary() 34 | 35 | return (coarseModel, fineModel) 36 | 37 | 38 | def _build_coarseNet(input_img): 39 | """ 40 | 建立coarseNet 41 | :param input_img: 输入图片的tensor 42 | :return: coarseNet 43 | """ 44 | conv1 = Conv2D(5, (11, 11), padding='same', activation='relu', name='coarseNet/conv1')(input_img) 45 | pool1 = MaxPooling2D((2, 2), name='coarseNet/pool1')(conv1) 46 | upsample1 = UpSampling2D((2, 2), name='coarseNet/upsample1')(pool1) 47 | normalize1 = BatchNormalization(axis=3, name='coarseNet/bn1')(upsample1) 48 | # dropout1 = Dropout(0.5, name='coarseNet/dropout1')(normalize1) 49 | 50 | conv2 = Conv2D(5, (9, 9), padding='same', activation='relu', name='coarseNet/conv2')(normalize1) 51 | pool2 = MaxPooling2D((2, 2), name='coarseNet/pool2')(conv2) 52 | upsample2 = UpSampling2D((2, 2), name='coarseNet/upsample2')(pool2) 53 | normalize2 = BatchNormalization(axis=3, name='coarseNet/bn2')(upsample2) 54 | # dropout2 = Dropout(0.5, name='coarseNet/dropout2')(normalize2) 55 | 56 | conv3 = Conv2D(10, (7, 7), padding='same', activation='relu', name='coarseNet/conv3')(normalize2) 57 | pool3 = MaxPooling2D((2, 2), name='coarseNet/pool3')(conv3) 58 | upsample3 = UpSampling2D((2, 2), name='coarseNet/upsample3')(pool3) 59 | # dropout3 = Dropout(0.5, name='coarseNet/dropout3')(upsample3) 60 | 61 | linear = LinearCombine(1, name='coarseNet/linear_combine')(upsample3) 62 | return linear 63 | 64 | 65 | def _build_fineNet(input_img, coarseNet): 66 | """ 67 | 建立fineNet 68 | :param input_img: 输入图片的tensor 69 | :param coarseNet: coarseNet的Tensor 70 | :return: fineNet 71 | """ 72 | # paper中的fine net 卷积kernel为4. 但经查看作者提供的源代码，第一层设置的6 73 | conv1 = Conv2D(6, (7, 7), padding='same', activation='relu', name='fineNet/conv1')(input_img) 74 | pool1 = MaxPooling2D((2, 2), name='fineNet/pool1')(conv1) 75 | upsample1 = UpSampling2D((2, 2), name='fineNet/upsample1')(pool1) 76 | 77 | # 级联coarseNet 78 | concat = concatenate([upsample1, coarseNet], axis=3, name='concat') 79 | normalize1 = BatchNormalization(axis=3, name='fineNet/bn1')(concat) 80 | # dropout1 = Dropout(0.5, name='fineNet/dropout1')(normalize1) 81 | 82 | conv2 = Conv2D(5, (5, 5), padding='same', activation='relu',name='fineNet/conv2')(normalize1) 83 | pool2 = MaxPooling2D((2, 2), name='fineNet/pool2')(conv2) 84 | upsample2 = UpSampling2D((2, 2), name='fineNet/upsample2')(pool2) 85 | normalize2 = BatchNormalization(axis=3, name='fineNet/bn2')(upsample2) 86 | # dropout2 = Dropout(0.5, name='fineNet/dropout2')(normalize2) 87 | 88 | conv3 = Conv2D(10, (3, 3), padding='same', activation='relu',name='fineNet/conv3')(normalize2) 89 | pool3 = MaxPooling2D((2, 2), name='fineNet/pool3')(conv3) 90 | upsample3 = UpSampling2D((2, 2), name='fineNet/upsample3')(pool3) 91 | # dropout3 = Dropout(0.5, name='fineNet/dropout3')(upsample3) 92 | 93 | linear = LinearCombine(1, name='fineNet/linear_combine')(upsample3) 94 | return linear 95 | 96 | 97 | class LinearCombine(Layer): 98 | """ 99 | paper 中的线性结合层 100 | """ 101 | 102 | def __init__(self, output_dim, **kwargs): 103 | self.output_dim = output_dim 104 | super(LinearCombine, self).__init__(**kwargs) 105 | 106 | def build(self, input_shape): 107 | self.kernel = self.add_weight(name='kernel', 108 | trainable=True, 109 | shape=(input_shape[3],), 110 | initializer='uniform') 111 | self.biases = self.add_weight(name='bias', 112 | trainable=True, 113 | shape=(self.output_dim,), 114 | initializer='normal') 115 | super(LinearCombine, self).build(input_shape) # Be sure to call this at the end 116 | 117 | def call(self, inputs, **kwargs): 118 | out = K.bias_add( 119 | K.sum(tf.multiply(inputs, self.kernel), axis=3), 120 | self.biases 121 | ) 122 | out = K.expand_dims(out, axis=3) 123 | return K.sigmoid(out) 124 | 125 | def compute_output_shape(self, input_shape): 126 | return (input_shape[0], input_shape[1], input_shape[2], self.output_dim) 127 | -------------------------------------------------------------------------------- /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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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. 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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 | --------------------------------------------------------------------------------