├── .gitignore ├── LICENSE ├── README.md ├── class_rebal.py ├── config.py ├── custom_layers ├── normalization.py └── unpooling_layer.py ├── data ├── prior_factor.npy ├── prior_prob.npy └── prior_prob_smoothed.npy ├── data_generator.py ├── demo.py ├── image_augmentation.py ├── images ├── 0_category_aug.png ├── 0_gt.png ├── 0_image.png ├── 0_image_aug.png ├── 0_out.png ├── 1_category_aug.png ├── 1_gt.png ├── 1_image.png ├── 1_image_aug.png ├── 1_out.png ├── 2_category_aug.png ├── 2_gt.png ├── 2_image.png ├── 2_image_aug.png ├── 2_out.png ├── 3_category_aug.png ├── 3_gt.png ├── 3_image.png ├── 3_image_aug.png ├── 3_out.png ├── 4_category_aug.png ├── 4_gt.png ├── 4_image.png ├── 4_image_aug.png ├── 4_out.png ├── 5_category_aug.png ├── 5_gt.png ├── 5_image.png ├── 5_image_aug.png ├── 5_out.png ├── 6_category_aug.png ├── 6_gt.png ├── 6_image.png ├── 6_image_aug.png ├── 6_out.png ├── 7_category_aug.png ├── 7_gt.png ├── 7_image.png ├── 7_image_aug.png ├── 7_out.png ├── 8_category_aug.png ├── 8_gt.png ├── 8_image.png ├── 8_image_aug.png ├── 8_out.png ├── 9_category_aug.png ├── 9_gt.png ├── 9_image.png ├── 9_image_aug.png ├── 9_out.png ├── dataset.png ├── dist.png ├── learning_curve.png ├── legend.png └── segnet.png ├── legend.py ├── model.py ├── names.txt ├── poster.pdf ├── pre-process.py ├── seg37list.mat ├── train.py ├── train_ids.txt ├── unit_tests.py ├── utils.py ├── valid_ids.txt └── vgg16.py /.gitignore: -------------------------------------------------------------------------------- 1 | .idea/ 2 | __pycache__/ 3 | data/ 4 | logs/ 5 | models/ 6 | sample.png 7 | temp/ 8 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 刘杨 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 语义分割 2 | 3 | 用SegNet进行室内语义分割。 4 | 5 | ## 依赖 6 | - [NumPy](http://docs.scipy.org/doc/numpy-1.10.1/user/install.html) 7 | - [Tensorflow](https://www.tensorflow.org/versions/r0.8/get_started/os_setup.html) 8 | - [Keras](https://keras.io/#installation) 9 | - [OpenCV](https://opencv-python-tutroals.readthedocs.io/en/latest/) 10 | 11 | ## 数据集 12 | 13 | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/dataset.png) 14 | 15 | 按照 [说明](http://3dvision.princeton.edu/projects/2015/SUNrgbd/) 下载 SUN RGB-D 数据集，放在 data 目录内。 16 | 17 | ```bash 18 | $ wget http://3dvision.princeton.edu/projects/2015/SUNrgbd/data/SUNRGBD.zip 19 | $ wget http://3dvision.princeton.edu/projects/2015/SUNrgbd/data/SUNRGBDtoolbox.zip 20 | ``` 21 | 22 | ## 架构 23 | 24 | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/segnet.png) 25 | 26 | 27 | ## ImageNet 预训练模型 28 | 下载 [VGG16](https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5) 放在 models 目录内。 29 | 30 | ## 用法 31 | ### 数据预处理 32 | 该数据集包含SUNRGBD V1的10335个RGBD图像，执行下述命令提取训练图像： 33 | ```bash 34 | $ python pre-process.py 35 | ``` 36 | 37 | 像素分布： 38 | 39 | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/dist.png) 40 | 41 | ### 数据集增强 42 | 图片 | 分割 | 图片 | 分割 | 43 | |---|---|---|---| 44 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/0_image_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/0_category_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/1_image_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/1_category_aug.png) | 45 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/2_image_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/2_category_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/3_image_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/3_category_aug.png) | 46 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/4_image_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/4_category_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/5_image_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/5_category_aug.png) | 47 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/6_image_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/6_category_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/7_image_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/7_category_aug.png) | 48 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/8_image_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/8_category_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/9_image_aug.png) |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/9_category_aug.png) | 49 | 50 | ### 训练 51 | ```bash 52 | $ python train.py 53 | ``` 54 | 55 | 如果想可视化训练过程，可执行： 56 | ```bash 57 | $ tensorboard --logdir path_to_current_dir/logs 58 | ``` 59 | 60 | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/learning_curve.png) 61 | 62 | ### 演示 63 | 64 | 下载 [预训练模型](https://github.com/foamliu/Semantic-Segmentation/releases/download/v1.0/model.81-3.5244.hdf5) 放在 models 目录，然后执行: 65 | 66 | 67 | ```bash 68 | $ python demo.py 69 | ``` 70 | 71 | 图例： 72 | 73 | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/legend.png) 74 | 75 | 输入 | 真实 | 输出 | 76 | |---|---|---| 77 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/0_image.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/0_gt.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/0_out.png)| 78 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/1_image.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/1_gt.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/1_out.png)| 79 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/2_image.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/2_gt.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/2_out.png)| 80 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/3_image.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/3_gt.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/3_out.png)| 81 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/4_image.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/4_gt.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/4_out.png)| 82 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/5_image.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/5_gt.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/5_out.png)| 83 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/6_image.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/6_gt.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/6_out.png)| 84 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/7_image.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/7_gt.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/7_out.png)| 85 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/8_image.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/8_gt.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/8_out.png)| 86 | |![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/9_image.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/9_gt.png) | ![image](https://github.com/foamliu/Semantic-Segmentation/raw/master/images/9_out.png)| 87 | 88 | -------------------------------------------------------------------------------- /class_rebal.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | import cv2 as cv 4 | import matplotlib.pylab as plt 5 | import numpy as np 6 | from console_progressbar import ProgressBar 7 | from scipy.interpolate import interp1d 8 | from scipy.signal import gaussian, convolve 9 | 10 | from config import num_classes, seg_path 11 | 12 | 13 | def compute_class_prior(do_plot=False): 14 | names = [f for f in os.listdir(seg_path) if f.lower().endswith('.png')] 15 | num_samples = len(names) 16 | prior_prob = np.zeros(num_classes) 17 | pb = ProgressBar(total=num_samples, prefix='Compute class prior', suffix='', decimals=3, length=50, fill='=') 18 | for i in range(num_samples): 19 | name = names[i] 20 | filename = os.path.join(seg_path, name) 21 | category = np.ravel(cv.imread(filename, 0)) 22 | counts = np.bincount(category) 23 | idxs = np.nonzero(counts)[0] 24 | prior_prob[idxs] += counts[idxs] 25 | pb.print_progress_bar(i + 1) 26 | 27 | prior_prob = prior_prob / (1.0 * np.sum(prior_prob)) 28 | 29 | # Save 30 | np.save(os.path.join(data_dir, "prior_prob.npy"), prior_prob) 31 | 32 | if do_plot: 33 | plt.hist(prior_prob, bins=100) 34 | plt.yscale("log") 35 | plt.show() 36 | 37 | 38 | def smooth_class_prior(sigma=5, do_plot=False): 39 | prior_prob = np.load(os.path.join(data_dir, "prior_prob.npy")) 40 | # add an epsilon to prior prob to avoid 0 vakues and possible NaN 41 | prior_prob += 1E-3 * np.min(prior_prob) 42 | # renormalize 43 | prior_prob = prior_prob / (1.0 * np.sum(prior_prob)) 44 | 45 | # Smooth with gaussian 46 | f = interp1d(np.arange(prior_prob.shape[0]), prior_prob) 47 | xx = np.linspace(0, prior_prob.shape[0] - 1, 1000) 48 | yy = f(xx) 49 | window = gaussian(2000, sigma) # 2000 pts in the window, sigma=5 50 | smoothed = convolve(yy, window / window.sum(), mode='same') 51 | fout = interp1d(xx, smoothed) 52 | prior_prob_smoothed = np.array([fout(i) for i in range(prior_prob.shape[0])]) 53 | prior_prob_smoothed = prior_prob_smoothed / np.sum(prior_prob_smoothed) 54 | 55 | # Save 56 | file_name = os.path.join(data_dir, "prior_prob_smoothed.npy") 57 | np.save(file_name, prior_prob_smoothed) 58 | 59 | if do_plot: 60 | plt.plot(prior_prob) 61 | plt.plot(prior_prob_smoothed, "g--") 62 | plt.plot(xx, smoothed, "r-") 63 | plt.yscale("log") 64 | plt.show() 65 | 66 | 67 | def compute_prior_factor(gamma=0.5, alpha=1, do_plot=False): 68 | file_name = os.path.join(data_dir, "prior_prob_smoothed.npy") 69 | prior_prob_smoothed = np.load(file_name) 70 | 71 | u = np.ones_like(prior_prob_smoothed) 72 | u = u / np.sum(1.0 * u) 73 | 74 | prior_factor = (1 - gamma) * prior_prob_smoothed + gamma * u 75 | prior_factor = np.power(prior_factor, -alpha) 76 | 77 | # renormalize 78 | prior_factor = prior_factor / (np.sum(prior_factor * prior_prob_smoothed)) 79 | 80 | file_name = os.path.join(data_dir, "prior_factor.npy") 81 | np.save(file_name, prior_factor) 82 | 83 | if do_plot: 84 | plt.plot(prior_factor) 85 | plt.yscale("log") 86 | plt.show() 87 | 88 | 89 | if __name__ == '__main__': 90 | data_dir = 'data/' 91 | do_plot = True 92 | 93 | compute_class_prior(do_plot=True) 94 | smooth_class_prior(do_plot=True) 95 | compute_prior_factor(do_plot=True) 96 | -------------------------------------------------------------------------------- /config.py: -------------------------------------------------------------------------------- 1 | import hdf5storage 2 | 3 | img_rows, img_cols = 256, 256 4 | channel = 3 5 | batch_size = 16 6 | epochs = 1000 7 | patience = 50 8 | num_samples = 10335 9 | num_train_samples = 8268 10 | # num_samples - num_train_samples 11 | num_valid_samples = 2067 12 | num_classes = 38 13 | kernel = 3 14 | weight_decay = 1e-2 15 | 16 | folder_metadata = 'data/SUNRGBDtoolbox/Metadata/' 17 | folder_2D_segmentation = 'annotation2Dfinal' 18 | folder_rgb_image = 'image' 19 | seg_path = 'data/SUNRGBD2Dseg/' 20 | 21 | seg37list = hdf5storage.loadmat('seg37list.mat')['seg37list'][0] 22 | seg37list = [seg[0] for seg in seg37list] 23 | # print(seg37list) 24 | # ['wall', 25 | # 'floor', 26 | # 'cabinet', 27 | # 'bed', 28 | # 'chair', 29 | # 'sofa', 30 | # 'table', 31 | # 'door', 32 | # 'window', 33 | # 'bookshelf', 34 | # 'picture', 35 | # 'counter', 36 | # 'blinds', 37 | # 'desk', 38 | # 'shelves', 39 | # 'curtain', 40 | # 'dresser', 41 | # 'pillow', 42 | # 'mirror', 43 | # 'floor_mat', 44 | # 'clothes', 45 | # 'ceiling', 46 | # 'books', 47 | # 'fridge', 48 | # 'tv', 49 | # 'paper', 50 | # 'towel', 51 | # 'shower_curtain', 52 | # 'box', 53 | # 'whiteboard', 54 | # 'person', 55 | # 'night_stand', 56 | # 'toilet', 57 | # 'sink', 58 | # 'lamp', 59 | # 'bathtub', 60 | # 'bag'] 61 | 62 | # all = [seg37list, other] 63 | 64 | objectColors = ['#000000', '#2523ad', '#066ff1', '#abebbb', '#64071f', '#e6840a', '#ccf808', '#636371', '#9d0eea', 65 | '#007567', '#a20084', '#4c7f57', '#e20724', '#0f5406', '#fd4465', '#75ce74', '#25cb7e', '#f62d45', 66 | '#fcda9f', '#f7a337', '#44e9c0', '#0abc21', '#330d75', '#0f18bf', '#5e7f46', '#f417c0', '#b57670', 67 | '#dcae65', '#79dd07', '#ce6f38', '#7143a4', '#61632d', '#80ea5b', '#27a355', '#09cb29', '#5f989e', 68 | '#c08035', '#6a1446'] 69 | colors = [[int(c[1:3], 16), int(c[3:5], 16), int(c[5:7], 16)] for c in objectColors] 70 | 71 | crop_size = 512 72 | -------------------------------------------------------------------------------- /custom_layers/normalization.py: -------------------------------------------------------------------------------- 1 | import theano.tensor as T 2 | from keras.layers.core import Layer 3 | 4 | 5 | class LRN2D(Layer): 6 | """ 7 | This code is adapted from pylearn2. 8 | License at: https://github.com/lisa-lab/pylearn2/blob/master/LICENSE.txt 9 | """ 10 | 11 | def __init__(self, alpha=1e-4, k=2, beta=0.75, n=5): 12 | if n % 2 == 0: 13 | raise NotImplementedError("LRN2D only works with odd n. n provided: " + str(n)) 14 | super(LRN2D, self).__init__() 15 | self.alpha = alpha 16 | self.k = k 17 | self.beta = beta 18 | self.n = n 19 | 20 | def get_output(self, train): 21 | X = self.get_input(train) 22 | b, ch, r, c = X.shape 23 | half_n = self.n // 2 24 | input_sqr = T.sqr(X) 25 | extra_channels = T.alloc(0., b, ch + 2 * half_n, r, c) 26 | input_sqr = T.set_subtensor(extra_channels[:, half_n:half_n + ch, :, :], input_sqr) 27 | scale = self.k 28 | for i in range(self.n): 29 | scale += self.alpha * input_sqr[:, i:i + ch, :, :] 30 | scale = scale ** self.beta 31 | return X / scale 32 | 33 | def get_config(self): 34 | return {"name": self.__class__.__name__, 35 | "alpha": self.alpha, 36 | "k": self.k, 37 | "beta": self.beta, 38 | "n": self.n} 39 | -------------------------------------------------------------------------------- /custom_layers/unpooling_layer.py: -------------------------------------------------------------------------------- 1 | from keras import backend as K 2 | from keras.engine.topology import Layer 3 | from keras.layers import Reshape, Concatenate, Lambda, Multiply 4 | 5 | 6 | class Unpooling(Layer): 7 | 8 | def __init__(self, **kwargs): 9 | super(Unpooling, self).__init__(**kwargs) 10 | 11 | def build(self, input_shape): 12 | super(Unpooling, self).build(input_shape) 13 | 14 | def call(self, inputs, **kwargs): 15 | x = inputs[:, 1] 16 | # print('x.shape: ' + str(K.int_shape(x))) 17 | bool_mask = Lambda(lambda t: K.greater_equal(t[:, 0], t[:, 1]), 18 | output_shape=K.int_shape(x)[1:])(inputs) 19 | # print('bool_mask.shape: ' + str(K.int_shape(bool_mask))) 20 | mask = Lambda(lambda t: K.cast(t, dtype='float32'))(bool_mask) 21 | # print('mask.shape: ' + str(K.int_shape(mask))) 22 | x = Multiply()([mask, x]) 23 | # print('x.shape: ' + str(K.int_shape(x))) 24 | return x 25 | 26 | def compute_output_shape(self, input_shape): 27 | return input_shape[0], input_shape[2], input_shape[3], input_shape[4] 28 | -------------------------------------------------------------------------------- /data/prior_factor.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/foamliu/Scene-Understanding/e8a4c2f6ce41fcde04202e6129d9bc406d4ff200/data/prior_factor.npy -------------------------------------------------------------------------------- /data/prior_prob.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/foamliu/Scene-Understanding/e8a4c2f6ce41fcde04202e6129d9bc406d4ff200/data/prior_prob.npy -------------------------------------------------------------------------------- /data/prior_prob_smoothed.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/foamliu/Scene-Understanding/e8a4c2f6ce41fcde04202e6129d9bc406d4ff200/data/prior_prob_smoothed.npy -------------------------------------------------------------------------------- /data_generator.py: -------------------------------------------------------------------------------- 1 | import os 2 | import random 3 | from random import shuffle 4 | 5 | import cv2 as cv 6 | import hdf5storage 7 | import numpy as np 8 | from keras.applications.vgg16 import preprocess_input 9 | from keras.utils import Sequence 10 | from keras.utils import to_categorical 11 | 12 | from config import folder_metadata 13 | from config import img_rows, img_cols, batch_size 14 | from config import num_classes 15 | from utils import get_image, get_category, random_crop 16 | 17 | 18 | class DataGenSequence(Sequence): 19 | def __init__(self, usage): 20 | self.usage = usage 21 | 22 | with open('{}_ids.txt'.format(usage), 'r') as f: 23 | ids = f.read().splitlines() 24 | self.ids = list(map(int, ids)) 25 | 26 | with open('names.txt', 'r') as f: 27 | self.names = f.read().splitlines() 28 | 29 | def __len__(self): 30 | return int(np.ceil(len(self.ids) / float(batch_size))) 31 | 32 | def __getitem__(self, idx): 33 | i = idx * batch_size 34 | 35 | length = min(batch_size, (len(self.ids) - i)) 36 | X = np.empty((length, img_rows, img_cols, 3), dtype=np.float32) 37 | Y = np.empty((length, img_rows, img_cols, num_classes), dtype=np.float32) 38 | 39 | for i_batch in range(length): 40 | id = self.ids[i + i_batch] 41 | name = self.names[id] 42 | image = get_image(name) 43 | category = get_category(id) 44 | image, category = random_crop(image, category) 45 | 46 | image = cv.cvtColor(image, cv.COLOR_BGR2RGB) 47 | 48 | X[i_batch] = image 49 | Y[i_batch] = to_categorical(category, num_classes) 50 | 51 | X = preprocess_input(X) 52 | 53 | return X, Y 54 | 55 | def on_epoch_end(self): 56 | np.random.shuffle(self.ids) 57 | 58 | 59 | def train_gen(): 60 | return DataGenSequence('train') 61 | 62 | 63 | def valid_gen(): 64 | return DataGenSequence('valid') 65 | 66 | 67 | def split_data(): 68 | filename = os.path.join(folder_metadata, 'SUNRGBDMeta.mat') 69 | meta = hdf5storage.loadmat(filename) 70 | names = [] 71 | for item in meta['SUNRGBDMeta'][0]: 72 | name = item[0][0] 73 | names.append(name) 74 | 75 | num_samples = len(names) # 10335 76 | print('num_samples: ' + str(num_samples)) 77 | 78 | num_train_samples = int(num_samples * 0.8) 79 | print('num_train_samples: ' + str(num_train_samples)) 80 | num_valid_samples = num_samples - num_train_samples 81 | print('num_valid_samples: ' + str(num_valid_samples)) 82 | valid_ids = random.sample(range(num_train_samples), num_valid_samples) 83 | valid_ids = list(map(str, valid_ids)) 84 | train_ids = [str(n) for n in range(num_train_samples) if n not in valid_ids] 85 | shuffle(valid_ids) 86 | shuffle(train_ids) 87 | 88 | with open('names.txt', 'w') as file: 89 | file.write('\n'.join(names)) 90 | 91 | with open('valid_ids.txt', 'w') as file: 92 | file.write('\n'.join(valid_ids)) 93 | 94 | with open('train_ids.txt', 'w') as file: 95 | file.write('\n'.join(train_ids)) 96 | 97 | 98 | if __name__ == '__main__': 99 | split_data() 100 | -------------------------------------------------------------------------------- /demo.py: -------------------------------------------------------------------------------- 1 | # import the necessary packages 2 | import os 3 | import random 4 | 5 | import cv2 as cv 6 | import keras.backend as K 7 | import numpy as np 8 | from keras.applications.vgg16 import preprocess_input 9 | 10 | from config import img_rows, img_cols, num_classes 11 | from model import build_model 12 | from utils import get_image, get_category, random_crop, to_bgr 13 | 14 | if __name__ == '__main__': 15 | model_weights_path = 'models/model.81-3.5244.hdf5' 16 | model = build_model() 17 | model.load_weights(model_weights_path) 18 | 19 | print(model.summary()) 20 | 21 | with open('names.txt', 'r') as f: 22 | names = f.read().splitlines() 23 | 24 | filename = 'valid_ids.txt' 25 | with open(filename, 'r') as f: 26 | ids = f.read().splitlines() 27 | ids = list(map(int, ids)) 28 | samples = random.sample(ids, 10) 29 | print('samples: ' + str(samples)) 30 | 31 | for i in range(len(samples)): 32 | id = samples[i] 33 | name = names[id] 34 | image_bgr = get_image(name) 35 | category = get_category(id) 36 | image_bgr, category = random_crop(image_bgr, category) 37 | 38 | image_rgb = cv.cvtColor(image_bgr, cv.COLOR_BGR2RGB) 39 | colorful_category = to_bgr(category) 40 | print('Start processing image: {}'.format(name)) 41 | 42 | x_test = np.empty((1, img_rows, img_cols, 3), dtype=np.float32) 43 | x_test[0, :, :, 0:3] = image_rgb 44 | x_test = preprocess_input(x_test) 45 | 46 | out = model.predict(x_test) 47 | out = np.reshape(out, (img_rows, img_cols, num_classes)) 48 | out = np.argmax(out, axis=2) 49 | out = to_bgr(out) 50 | 51 | if not os.path.exists('images'): 52 | os.makedirs('images') 53 | 54 | cv.imwrite('images/{}_image.png'.format(i), image_bgr) 55 | cv.imwrite('images/{}_out.png'.format(i), out) 56 | cv.imwrite('images/{}_gt.png'.format(i), colorful_category) 57 | 58 | K.clear_session() 59 | -------------------------------------------------------------------------------- /image_augmentation.py: -------------------------------------------------------------------------------- 1 | import random 2 | 3 | import cv2 as cv 4 | import numpy as np 5 | from imgaug import augmenters as iaa 6 | from tqdm import tqdm 7 | 8 | from config import img_rows, img_cols 9 | from utils import get_image, get_category, to_bgr 10 | 11 | seq = iaa.Sequential([ 12 | iaa.Fliplr(0.5), 13 | iaa.CropAndPad( 14 | percent=(-0.25, 0.25), 15 | pad_mode=["wrap"], 16 | ), 17 | iaa.Affine( 18 | scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, 19 | translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, 20 | rotate=(-25, 25), 21 | shear=(-8, 8), 22 | order=[0], 23 | mode='wrap' 24 | ) 25 | ]) 26 | seq_det = seq.to_deterministic() 27 | 28 | seq_img = iaa.Sequential([ 29 | iaa.GaussianBlur(sigma=(0, 0.5)), 30 | iaa.ContrastNormalization((0.75, 1.5)), 31 | iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5), 32 | iaa.Multiply((0.8, 1.2), per_channel=0.2), 33 | 34 | ], random_order=True) 35 | 36 | if __name__ == '__main__': 37 | with open('names.txt', 'r') as f: 38 | names = f.read().splitlines() 39 | 40 | filename = 'valid_ids.txt' 41 | with open(filename, 'r') as f: 42 | ids = f.read().splitlines() 43 | ids = list(map(int, ids)) 44 | id = random.choice(ids) 45 | name = names[id] 46 | image = get_image(name) 47 | category = get_category(id) 48 | 49 | image = cv.resize(image, (img_rows, img_cols), cv.INTER_NEAREST) 50 | category = cv.resize(category, (img_rows, img_cols), cv.INTER_NEAREST) 51 | 52 | length = 10 53 | images = np.zeros((length, img_rows, img_cols, 3), np.uint8) 54 | categories = np.zeros((length, img_rows, img_cols), np.uint8) 55 | for i in tqdm(range(length)): 56 | images[i] = image.copy() 57 | categories[i] = category.copy() 58 | 59 | images_aug = seq_img.augment_images(images) 60 | images_aug = seq_det.augment_images(images_aug) 61 | categories_aug = seq_det.augment_images(categories) 62 | 63 | for i in range(length): 64 | image = images_aug[i] 65 | category_bgr = to_bgr(categories_aug[i].astype(np.uint8)) 66 | cv.imwrite('images/{}_image_aug.png'.format(i), image) 67 | cv.imwrite('images/{}_category_aug.png'.format(i), category_bgr) 68 | -------------------------------------------------------------------------------- /images/0_category_aug.png: -------------------------------------------------------------------------------- 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right = left + (width_cell - 1) 27 | cv.rectangle(frame, (left, top), (right, bottom), color, cv.FILLED) 28 | draw_str(frame, (left + margin_x, top + margin_y), name) 29 | 30 | cv.imshow('frame', frame) 31 | cv.imwrite('images/legend.png', frame) 32 | cv.waitKey(0) 33 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | import keras.backend as K 2 | from keras.layers import Input, ZeroPadding2D, Conv2D, UpSampling2D, BatchNormalization, MaxPooling2D, Reshape, \ 3 | Concatenate, Flatten, Dense, Dropout 4 | from keras.models import Model 5 | from keras.utils import plot_model 6 | 7 | from config import img_rows, img_cols, num_classes, channel, kernel 8 | from custom_layers.unpooling_layer import Unpooling 9 | 10 | 11 | def build_model(): 12 | # Encoder 13 | img_input = Input(shape=(img_rows, img_cols, channel)) 14 | x = ZeroPadding2D((1, 1))(img_input) 15 | x = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv1_1')(x) 16 | x = ZeroPadding2D((1, 1))(x) 17 | x = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv1_2')(x) 18 | orig_1 = x 19 | x = MaxPooling2D((2, 2), strides=(2, 2))(x) 20 | 21 | x = ZeroPadding2D((1, 1))(x) 22 | x = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv2_1')(x) 23 | x = ZeroPadding2D((1, 1))(x) 24 | x = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv2_2')(x) 25 | orig_2 = x 26 | x = MaxPooling2D((2, 2), strides=(2, 2))(x) 27 | 28 | x = ZeroPadding2D((1, 1))(x) 29 | x = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv3_1')(x) 30 | x = ZeroPadding2D((1, 1))(x) 31 | x = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv3_2')(x) 32 | x = ZeroPadding2D((1, 1))(x) 33 | x = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv3_3')(x) 34 | orig_3 = x 35 | x = MaxPooling2D((2, 2), strides=(2, 2))(x) 36 | 37 | x = ZeroPadding2D((1, 1))(x) 38 | x = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv4_1')(x) 39 | x = ZeroPadding2D((1, 1))(x) 40 | x = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv4_2')(x) 41 | x = ZeroPadding2D((1, 1))(x) 42 | x = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv4_3')(x) 43 | orig_4 = x 44 | x = MaxPooling2D((2, 2), strides=(2, 2))(x) 45 | 46 | x = ZeroPadding2D((1, 1))(x) 47 | x = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv5_1')(x) 48 | x = ZeroPadding2D((1, 1))(x) 49 | x = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv5_2')(x) 50 | x = ZeroPadding2D((1, 1))(x) 51 | x = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', name='conv5_3')(x) 52 | orig_5 = x 53 | x = MaxPooling2D((2, 2), strides=(2, 2))(x) 54 | 55 | # Add Fully Connected Layer 56 | x_fc = Flatten()(x) 57 | x_fc = Dense(4096, activation='relu')(x_fc) 58 | x_fc = Dropout(0.5)(x_fc) 59 | x_fc = Dense(4096, activation='relu')(x_fc) 60 | x_fc = Dropout(0.5)(x_fc) 61 | x_fc = Dense(1000, activation='softmax')(x_fc) 62 | model = Model(img_input, x_fc) 63 | 64 | # Loads ImageNet pre-trained data 65 | weights_path = 'models/vgg16_weights_tf_dim_ordering_tf_kernels.h5' 66 | model.load_weights(weights_path, by_name=True) 67 | 68 | # Decoder 69 | x = UpSampling2D(size=(2, 2))(x) 70 | the_shape = K.int_shape(orig_5) 71 | shape = (1, the_shape[1], the_shape[2], the_shape[3]) 72 | origReshaped = Reshape(shape)(orig_5) 73 | xReshaped = Reshape(shape)(x) 74 | together = Concatenate(axis=1)([origReshaped, xReshaped]) 75 | x = Unpooling()(together) 76 | x = Conv2D(512, (kernel, kernel), activation='relu', padding='same', name='deconv5_1', 77 | kernel_initializer='he_normal')(x) 78 | x = BatchNormalization()(x) 79 | x = Conv2D(512, (kernel, kernel), activation='relu', padding='same', name='deconv5_2', 80 | kernel_initializer='he_normal')(x) 81 | x = BatchNormalization()(x) 82 | x = Conv2D(512, (kernel, kernel), activation='relu', padding='same', name='deconv5_3', 83 | kernel_initializer='he_normal')(x) 84 | x = BatchNormalization()(x) 85 | 86 | x = UpSampling2D(size=(2, 2))(x) 87 | the_shape = K.int_shape(orig_4) 88 | shape = (1, the_shape[1], the_shape[2], the_shape[3]) 89 | origReshaped = Reshape(shape)(orig_4) 90 | xReshaped = Reshape(shape)(x) 91 | together = Concatenate(axis=1)([origReshaped, xReshaped]) 92 | x = Unpooling()(together) 93 | x = Conv2D(512, (kernel, kernel), activation='relu', padding='same', name='deconv4_1', 94 | kernel_initializer='he_normal')(x) 95 | x = BatchNormalization()(x) 96 | x = Conv2D(512, (kernel, kernel), activation='relu', padding='same', name='deconv4_2', 97 | kernel_initializer='he_normal')(x) 98 | x = BatchNormalization()(x) 99 | x = Conv2D(256, (kernel, kernel), activation='relu', padding='same', name='deconv4_3', 100 | kernel_initializer='he_normal')(x) 101 | x = BatchNormalization()(x) 102 | 103 | x = UpSampling2D(size=(2, 2))(x) 104 | the_shape = K.int_shape(orig_3) 105 | shape = (1, the_shape[1], the_shape[2], the_shape[3]) 106 | origReshaped = Reshape(shape)(orig_3) 107 | xReshaped = Reshape(shape)(x) 108 | together = Concatenate(axis=1)([origReshaped, xReshaped]) 109 | x = Unpooling()(together) 110 | x = Conv2D(256, (kernel, kernel), activation='relu', padding='same', name='deconv3_1', 111 | kernel_initializer='he_normal')(x) 112 | x = BatchNormalization()(x) 113 | x = Conv2D(256, (kernel, kernel), activation='relu', padding='same', name='deconv3_2', 114 | kernel_initializer='he_normal')(x) 115 | x = BatchNormalization()(x) 116 | x = Conv2D(128, (kernel, kernel), activation='relu', padding='same', name='deconv3_3', 117 | kernel_initializer='he_normal')(x) 118 | x = BatchNormalization()(x) 119 | 120 | x = UpSampling2D(size=(2, 2))(x) 121 | the_shape = K.int_shape(orig_2) 122 | shape = (1, the_shape[1], the_shape[2], the_shape[3]) 123 | origReshaped = Reshape(shape)(orig_2) 124 | xReshaped = Reshape(shape)(x) 125 | together = Concatenate(axis=1)([origReshaped, xReshaped]) 126 | x = Unpooling()(together) 127 | x = Conv2D(128, (kernel, kernel), activation='relu', padding='same', name='deconv2_1', 128 | kernel_initializer='he_normal')(x) 129 | x = BatchNormalization()(x) 130 | x = Conv2D(64, (kernel, kernel), activation='relu', padding='same', name='deconv2_2', 131 | kernel_initializer='he_normal')(x) 132 | x = BatchNormalization()(x) 133 | 134 | x = UpSampling2D(size=(2, 2))(x) 135 | the_shape = K.int_shape(orig_1) 136 | shape = (1, the_shape[1], the_shape[2], the_shape[3]) 137 | origReshaped = Reshape(shape)(orig_1) 138 | xReshaped = Reshape(shape)(x) 139 | together = Concatenate(axis=1)([origReshaped, xReshaped]) 140 | x = Unpooling()(together) 141 | x = Conv2D(64, (kernel, kernel), activation='relu', padding='same', name='deconv1_1', 142 | kernel_initializer='he_normal')(x) 143 | x = BatchNormalization()(x) 144 | x = Conv2D(64, (kernel, kernel), activation='relu', padding='same', name='deconv1_2', 145 | kernel_initializer='he_normal')(x) 146 | x = BatchNormalization()(x) 147 | 148 | outputs = Conv2D(num_classes, (1, 1), activation='softmax', padding='valid', name='pred', 149 | kernel_initializer='he_normal')(x) 150 | 151 | model = Model(inputs=img_input, outputs=outputs, name="SegNet") 152 | return model 153 | 154 | 155 | if __name__ == '__main__': 156 | encoder_decoder = build_model() 157 | # input_layer = model.get_layer('input') 158 | print(encoder_decoder.summary()) 159 | plot_model(encoder_decoder, to_file='model.svg', show_layer_names=True, show_shapes=True) 160 | 161 | K.clear_session() 162 | -------------------------------------------------------------------------------- /poster.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/foamliu/Scene-Understanding/e8a4c2f6ce41fcde04202e6129d9bc406d4ff200/poster.pdf -------------------------------------------------------------------------------- /pre-process.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import argparse 3 | import os 4 | import zipfile 5 | 6 | import cv2 as cv 7 | import hdf5storage 8 | import numpy as np 9 | from console_progressbar import ProgressBar 10 | 11 | # python pre-process.py -d ../../data/Semantic-Segmentation/data/ 12 | if __name__ == '__main__': 13 | # Parse arguments 14 | ap = argparse.ArgumentParser() 15 | ap.add_argument("-d", "--data", help="path to data files") 16 | args = vars(ap.parse_args()) 17 | data_path = args["data"] 18 | 19 | if data_path is None: 20 | data_path = 'data/' 21 | 22 | filename = 'SUNRGBD.zip' 23 | filename = os.path.join(data_path, filename) 24 | print('Extracting {}...'.format(filename)) 25 | with zipfile.ZipFile(filename, 'r') as zip_file: 26 | zip_file.extractall(data_path) 27 | 28 | filename = 'SUNRGBDtoolbox.zip' 29 | filename = os.path.join(data_path, filename) 30 | print('Extracting {}...'.format(filename)) 31 | with zipfile.ZipFile(filename, 'r') as zip_file: 32 | zip_file.extractall(data_path) 33 | 34 | filename = 'data/SUNRGBDtoolbox/Metadata/SUNRGBD2Dseg.mat' 35 | SUNRGBD2Dseg = hdf5storage.loadmat(filename) 36 | num_samples = len(SUNRGBD2Dseg['SUNRGBD2Dseg'][0]) 37 | print('num_samples: ' + str(num_samples)) 38 | 39 | seg_path = 'data/SUNRGBD2Dseg' 40 | if not os.path.exists(seg_path): 41 | os.makedirs(seg_path) 42 | 43 | pb = ProgressBar(total=num_samples, prefix='Processing images', suffix='', decimals=3, length=50, fill='=') 44 | 45 | for i in range(num_samples): 46 | semantic = SUNRGBD2Dseg['SUNRGBD2Dseg'][0][i][0] 47 | semantic = semantic.astype(np.uint8) 48 | filename = os.path.join(seg_path, '{}.png'.format(i)) 49 | cv.imwrite(filename, semantic) 50 | pb.print_progress_bar(i + 1) 51 | -------------------------------------------------------------------------------- /seg37list.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/foamliu/Scene-Understanding/e8a4c2f6ce41fcde04202e6129d9bc406d4ff200/seg37list.mat -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import keras 4 | import tensorflow as tf 5 | from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau 6 | from keras.utils import multi_gpu_model 7 | 8 | from config import patience, epochs, num_train_samples, num_valid_samples, batch_size 9 | from data_generator import train_gen, valid_gen 10 | from model import build_model 11 | from utils import get_available_gpus, get_available_cpus, categorical_crossentropy_with_class_rebal 12 | 13 | if __name__ == '__main__': 14 | # Parse arguments 15 | ap = argparse.ArgumentParser() 16 | ap.add_argument("-p", "--pretrained", help="path to save pretrained model files") 17 | args = vars(ap.parse_args()) 18 | pretrained_path = args["pretrained"] 19 | checkpoint_models_path = 'models/' 20 | 21 | # Callbacks 22 | tensor_board = keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=0, write_graph=True, write_images=True) 23 | model_names = checkpoint_models_path + 'model.{epoch:02d}-{val_loss:.4f}.hdf5' 24 | model_checkpoint = ModelCheckpoint(model_names, monitor='val_loss', verbose=1, save_best_only=True) 25 | early_stop = EarlyStopping('val_loss', patience=patience) 26 | reduce_lr = ReduceLROnPlateau('val_loss', factor=0.5, patience=int(patience / 4), verbose=1) 27 | 28 | 29 | class MyCbk(keras.callbacks.Callback): 30 | def __init__(self, model): 31 | keras.callbacks.Callback.__init__(self) 32 | self.model_to_save = model 33 | 34 | def on_epoch_end(self, epoch, logs=None): 35 | fmt = checkpoint_models_path + 'model.%02d-%.4f.hdf5' 36 | self.model_to_save.save(fmt % (epoch, logs['val_loss'])) 37 | 38 | 39 | # Load our model, added support for Multi-GPUs 40 | num_gpu = len(get_available_gpus()) 41 | if num_gpu >= 2: 42 | with tf.device("/cpu:0"): 43 | model = build_model() 44 | if pretrained_path is not None: 45 | model.load_weights(pretrained_path) 46 | 47 | new_model = multi_gpu_model(model, gpus=num_gpu) 48 | # rewrite the callback: saving through the original model and not the multi-gpu model. 49 | model_checkpoint = MyCbk(model) 50 | else: 51 | new_model = build_model() 52 | if pretrained_path is not None: 53 | new_model.load_weights(pretrained_path) 54 | 55 | sgd = keras.optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5.) 56 | new_model.compile(optimizer=sgd, loss=categorical_crossentropy_with_class_rebal, metrics=['accuracy']) 57 | 58 | print(new_model.summary()) 59 | 60 | # Final callbacks 61 | callbacks = [tensor_board, model_checkpoint, reduce_lr, early_stop] 62 | 63 | # Start Fine-tuning 64 | new_model.fit_generator(train_gen(), 65 | steps_per_epoch=num_train_samples // batch_size, 66 | validation_data=valid_gen(), 67 | validation_steps=num_valid_samples // batch_size, 68 | epochs=epochs, 69 | verbose=1, 70 | callbacks=callbacks, 71 | use_multiprocessing=True, 72 | workers=int(get_available_cpus() * 0.80) 73 | ) 74 | -------------------------------------------------------------------------------- /train_ids.txt: -------------------------------------------------------------------------------- 1 | 523 2 | 3948 3 | 686 4 | 7026 5 | 1271 6 | 6854 7 | 6631 8 | 7278 9 | 3786 10 | 4434 11 | 1266 12 | 979 13 | 215 14 | 1198 15 | 7106 16 | 7520 17 | 5977 18 | 2896 19 | 597 20 | 5696 21 | 6076 22 | 5983 23 | 7425 24 | 4334 25 | 5497 26 | 2645 27 | 7385 28 | 3357 29 | 5832 30 | 4060 31 | 4804 32 | 4659 33 | 2009 34 | 4797 35 | 3854 36 | 6123 37 | 432 38 | 377 39 | 2938 40 | 7880 41 | 1417 42 | 3475 43 | 7266 44 | 914 45 | 4001 46 | 5906 47 | 5428 48 | 902 49 | 3437 50 | 5412 51 | 7745 52 | 2332 53 | 2936 54 | 4409 55 | 623 56 | 8176 57 | 2679 58 | 931 59 | 3160 60 | 1136 61 | 5349 62 | 3343 63 | 4024 64 | 5306 65 | 1822 66 | 413 67 | 3080 68 | 3780 69 | 6070 70 | 4793 71 | 1800 72 | 3904 73 | 6378 74 | 6911 75 | 6021 76 | 555 77 | 8212 78 | 2110 79 | 4014 80 | 7232 81 | 7231 82 | 5630 83 | 3278 84 | 1341 85 | 6717 86 | 6352 87 | 3418 88 | 3592 89 | 2744 90 | 7235 91 | 5677 92 | 1042 93 | 7336 94 | 3215 95 | 4662 96 | 6623 97 | 6235 98 | 6940 99 | 3613 100 | 8034 101 | 2976 102 | 6345 103 | 7691 104 | 521 105 | 3560 106 | 1818 107 | 982 108 | 6319 109 | 5297 110 | 113 111 | 2675 112 | 6039 113 | 5715 114 | 6276 115 | 5969 116 | 6477 117 | 3600 118 | 2100 119 | 437 120 | 1575 121 | 1513 122 | 5558 123 | 7633 124 | 2059 125 | 2563 126 | 905 127 | 4857 128 | 4773 129 | 7788 130 | 7436 131 | 4465 132 | 4472 133 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| 7749 961 | 6595 962 | 6459 963 | 4680 964 | 6 965 | 1714 966 | 7137 967 | 5781 968 | 1969 969 | 2842 970 | 5281 971 | 4588 972 | 7993 973 | 1464 974 | 6457 975 | 2941 976 | 6943 977 | 7161 978 | 1489 979 | 6812 980 | 662 981 | 3223 982 | 5936 983 | 4335 984 | 7192 985 | 6985 986 | 8146 987 | 6318 988 | 5743 989 | 499 990 | 2257 991 | 2822 992 | 5949 993 | 6234 994 | 158 995 | 5727 996 | 7768 997 | 2201 998 | 5635 999 | 4619 1000 | 2331 1001 | 2763 1002 | 353 1003 | 425 1004 | 7947 1005 | 5958 1006 | 3073 1007 | 5080 1008 | 7058 1009 | 7937 1010 | 1279 1011 | 6089 1012 | 8217 1013 | 1115 1014 | 703 1015 | 6586 1016 | 6942 1017 | 4566 1018 | 1365 1019 | 4902 1020 | 558 1021 | 3170 1022 | 1337 1023 | 326 1024 | 85 1025 | 4453 1026 | 4176 1027 | 8054 1028 | 5016 1029 | 81 1030 | 3603 1031 | 5750 1032 | 1493 1033 | 5215 1034 | 1523 1035 | 5535 1036 | 68 1037 | 6996 1038 | 2626 1039 | 5657 1040 | 5054 1041 | 7432 1042 | 5777 1043 | 2705 1044 | 7978 1045 | 7565 1046 | 5426 1047 | 7417 1048 | 5020 1049 | 2512 1050 | 4979 1051 | 5355 1052 | 6331 1053 | 2930 1054 | 3866 1055 | 5325 1056 | 7714 1057 | 4437 1058 | 6878 1059 | 6626 1060 | 8216 1061 | 3923 1062 | 50 1063 | 7215 1064 | 8080 1065 | 4687 1066 | 6360 1067 | 3703 1068 | 3724 1069 | 7410 1070 | 4795 1071 | 1387 1072 | 2084 1073 | 3704 1074 | 4641 1075 | 1065 1076 | 5236 1077 | 5970 1078 | 4574 1079 | 7878 1080 | 4140 1081 | 3823 1082 | 5198 1083 | 7297 1084 | 7549 1085 | 4054 1086 | 2185 1087 | 3464 1088 | 522 1089 | 6639 1090 | 7643 1091 | 5041 1092 | 1700 1093 | 514 1094 | 4741 1095 | 1599 1096 | 6560 1097 | 6730 1098 | 7614 1099 | 2595 1100 | 3801 1101 | 5999 1102 | 1630 1103 | 7468 1104 | 3426 1105 | 7987 1106 | 3798 1107 | 6604 1108 | 1236 1109 | 3697 1110 | 4095 1111 | 1655 1112 | 343 1113 | 612 1114 | 1909 1115 | 4376 1116 | 1791 1117 | 645 1118 | 5444 1119 | 7558 1120 | 2409 1121 | 3722 1122 | 7957 1123 | 7819 1124 | 5898 1125 | 2560 1126 | 5108 1127 | 2170 1128 | 1934 1129 | 7999 1130 | 2272 1131 | 2238 1132 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| 5226 1891 | 841 1892 | 2255 1893 | 2963 1894 | 4939 1895 | 3789 1896 | 1041 1897 | 7445 1898 | 5343 1899 | 5399 1900 | 4286 1901 | 3354 1902 | 5599 1903 | 4502 1904 | 2641 1905 | 2275 1906 | 5299 1907 | 4672 1908 | 169 1909 | 2358 1910 | 3561 1911 | 2371 1912 | 1531 1913 | 7816 1914 | 2039 1915 | 8129 1916 | 860 1917 | 8023 1918 | 6794 1919 | 5070 1920 | 8118 1921 | 7373 1922 | 6919 1923 | 7727 1924 | 7650 1925 | 4229 1926 | 3086 1927 | 7922 1928 | 1459 1929 | 7493 1930 | 4648 1931 | 5896 1932 | 5732 1933 | 7583 1934 | 670 1935 | 6417 1936 | 108 1937 | 4766 1938 | 5621 1939 | 3873 1940 | 3209 1941 | 3761 1942 | 4138 1943 | 2459 1944 | 6740 1945 | 5135 1946 | 7523 1947 | 6656 1948 | 2751 1949 | 4496 1950 | 8256 1951 | 4977 1952 | 2338 1953 | 7567 1954 | 4173 1955 | 4612 1956 | 1383 1957 | 1999 1958 | 1849 1959 | 862 1960 | 49 1961 | 2411 1962 | 7575 1963 | 2673 1964 | 3488 1965 | 4187 1966 | 4573 1967 | 6583 1968 | 3217 1969 | 3533 1970 | 5606 1971 | 3505 1972 | 6750 1973 | 4232 1974 | 147 1975 | 1053 1976 | 5181 1977 | 863 1978 | 4258 1979 | 8173 1980 | 5217 1981 | 2145 1982 | 1538 1983 | 3629 1984 | 7613 1985 | 6725 1986 | 1673 1987 | 4708 1988 | 5190 1989 | 2958 1990 | 7310 1991 | 1590 1992 | 1770 1993 | 331 1994 | 3081 1995 | 8263 1996 | 7929 1997 | 1072 1998 | 6732 1999 | 2278 2000 | 2652 2001 | 5536 2002 | 7538 2003 | 4549 2004 | 3126 2005 | 2060 2006 | 2634 2007 | 6231 2008 | 4475 2009 | 7568 2010 | 5938 2011 | 6989 2012 | 2405 2013 | 6038 2014 | 2501 2015 | 6284 2016 | 1382 2017 | 7698 2018 | 2438 2019 | 4350 2020 | 1483 2021 | 2290 2022 | 5747 2023 | 4201 2024 | 6600 2025 | 7806 2026 | 1906 2027 | 1486 2028 | 3563 2029 | 2565 2030 | 5647 2031 | 3087 2032 | 1107 2033 | 2181 2034 | 3414 2035 | 8190 2036 | 2608 2037 | 4497 2038 | 757 2039 | 5432 2040 | 1354 2041 | 5909 2042 | 3255 2043 | 1034 2044 | 5466 2045 | 2157 2046 | 2882 2047 | 4430 2048 | 5385 2049 | 30 2050 | 1476 2051 | 1570 2052 | 1375 2053 | 1929 2054 | 3460 2055 | 6632 2056 | 4985 2057 | 7991 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7557 2143 | 7179 2144 | 1658 2145 | 7180 2146 | 7620 2147 | 2216 2148 | 2046 2149 | 8038 2150 | 7884 2151 | 1060 2152 | 6407 2153 | 5461 2154 | 7528 2155 | 2708 2156 | 2691 2157 | 7595 2158 | 298 2159 | 5191 2160 | 3791 2161 | 3329 2162 | 6172 2163 | 5057 2164 | 6062 2165 | 3449 2166 | 5748 2167 | 4185 2168 | 426 2169 | 8031 2170 | 294 2171 | 1093 2172 | 3596 2173 | 7338 2174 | 4858 2175 | 4879 2176 | 6917 2177 | 42 2178 | 5450 2179 | 1623 2180 | 7843 2181 | 5420 2182 | 6515 2183 | 904 2184 | 4865 2185 | 4511 2186 | 2541 2187 | 2410 2188 | 5833 2189 | 1647 2190 | 4031 2191 | 3699 2192 | 3788 2193 | 3459 2194 | 6381 2195 | 3569 2196 | 5557 2197 | 3611 2198 | 2662 2199 | 1965 2200 | 7369 2201 | 2604 2202 | 4178 2203 | 5218 2204 | 691 2205 | 4458 2206 | 4999 2207 | 638 2208 | 1622 2209 | 4425 2210 | 6508 2211 | 2985 2212 | 1172 2213 | 1104 2214 | 1881 2215 | 7440 2216 | 947 2217 | 8208 2218 | 5848 2219 | 2370 2220 | 5516 2221 | 5000 2222 | 7696 2223 | 1443 2224 | 3097 2225 | 1202 2226 | 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| 3685 8122 | 3820 8123 | 981 8124 | 4273 8125 | 3506 8126 | 1484 8127 | 2002 8128 | 3883 8129 | 5529 8130 | 8155 8131 | 4735 8132 | 7458 8133 | 5089 8134 | 8100 8135 | 594 8136 | 3648 8137 | 7429 8138 | 7846 8139 | 456 8140 | 2102 8141 | 3615 8142 | 7844 8143 | 4158 8144 | 702 8145 | 3770 8146 | 5807 8147 | 2788 8148 | 7915 8149 | 8200 8150 | 3719 8151 | 7211 8152 | 3265 8153 | 1765 8154 | 3503 8155 | 6209 8156 | 633 8157 | 1499 8158 | 5291 8159 | 7092 8160 | 4791 8161 | 1037 8162 | 4411 8163 | 4471 8164 | 6687 8165 | 3666 8166 | 6158 8167 | 3956 8168 | 3539 8169 | 4520 8170 | 7088 8171 | 7912 8172 | 6049 8173 | 2702 8174 | 5235 8175 | 1941 8176 | 5771 8177 | 4493 8178 | 6921 8179 | 6147 8180 | 4596 8181 | 3348 8182 | 7597 8183 | 3731 8184 | 7863 8185 | 3542 8186 | 4528 8187 | 1159 8188 | 3119 8189 | 7763 8190 | 818 8191 | 6936 8192 | 868 8193 | 7918 8194 | 5313 8195 | 1820 8196 | 1367 8197 | 5616 8198 | 2051 8199 | 1721 8200 | 1696 8201 | 308 8202 | 557 8203 | 6365 8204 | 5288 8205 | 8049 8206 | 1887 8207 | 5607 8208 | 8033 8209 | 7480 8210 | 2733 8211 | 551 8212 | 5574 8213 | 8224 8214 | 4352 8215 | 212 8216 | 2841 8217 | 2303 8218 | 4884 8219 | 3878 8220 | 6373 8221 | 4917 8222 | 5791 8223 | 1955 8224 | 8166 8225 | 3041 8226 | 7054 8227 | 910 8228 | 5564 8229 | 2363 8230 | 2758 8231 | 7100 8232 | 295 8233 | 5693 8234 | 3745 8235 | 1044 8236 | 6350 8237 | 4115 8238 | 6649 8239 | 2677 8240 | 2330 8241 | 1824 8242 | 1692 8243 | 8112 8244 | 4960 8245 | 3572 8246 | 351 8247 | 6821 8248 | 7005 8249 | 7036 8250 | 7198 8251 | 2071 8252 | 6858 8253 | 18 8254 | 3840 8255 | 3099 8256 | 1196 8257 | 5286 8258 | 3859 8259 | 3657 8260 | 7415 8261 | 940 8262 | 7274 8263 | 7123 8264 | 2516 8265 | 1731 8266 | 5783 8267 | 1702 8268 | 6965 -------------------------------------------------------------------------------- /unit_tests.py: -------------------------------------------------------------------------------- 1 | import os 2 | import random 3 | import unittest 4 | 5 | import cv2 as cv 6 | 7 | from data_generator import get_image, get_category, to_bgr 8 | from data_generator import random_choice, safe_crop 9 | 10 | 11 | class TestStringMethods(unittest.TestCase): 12 | def setUp(self): 13 | if not os.path.exists('temp'): 14 | os.makedirs('temp') 15 | 16 | def tearDown(self): 17 | pass 18 | 19 | def test_get_semantic(self): 20 | name = 'SUNRGBD/kv1/NYUdata/NYU0899' 21 | image, image_size = get_image(name) 22 | print('image_size: ' + str(image_size)) 23 | semantic = get_category(name, image_size) 24 | semantic = to_bgr(semantic) 25 | cv.imwrite('temp/test_get_semantic_image.png', image) 26 | cv.imwrite('temp/test_get_semantic_semantic.png', semantic) 27 | 28 | def test_safe_crop(self): 29 | name = 'SUNRGBD/kv1/NYUdata/NYU0899' 30 | image, image_size = get_image(name) 31 | semantic = get_category(name, image_size) 32 | different_sizes = [(320, 320), (480, 480), (640, 640)] 33 | crop_size = random.choice(different_sizes) 34 | 35 | x, y = random_choice(image_size, crop_size) 36 | image = safe_crop(image, x, y, crop_size) 37 | semantic = safe_crop(semantic, x, y, crop_size) 38 | semantic = to_bgr(semantic) 39 | cv.imwrite('temp/test_safe_crop_image.png', image) 40 | cv.imwrite('temp/test_safe_crop_semantic.png', semantic) 41 | 42 | 43 | if __name__ == '__main__': 44 | unittest.main() 45 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import multiprocessing 2 | import os 3 | import random 4 | 5 | import cv2 as cv 6 | import keras.backend as K 7 | import numpy as np 8 | from tensorflow.python.client import device_lib 9 | 10 | from config import num_classes, crop_size, folder_rgb_image, seg_path, colors, img_rows, img_cols 11 | 12 | prob = np.load('data/prior_prob.npy') 13 | median = np.median(prob) 14 | factor = (median / prob).astype(np.float32) 15 | 16 | 17 | def categorical_crossentropy_with_class_rebal(y_true, y_pred): 18 | y_true = K.reshape(y_true, (-1, num_classes)) 19 | y_pred = K.reshape(y_pred, (-1, num_classes)) 20 | 21 | idx_max = K.argmax(y_true, axis=1) 22 | weights = K.gather(factor, idx_max) 23 | weights = K.reshape(weights, (-1, 1)) 24 | 25 | # multiply y_true by weights 26 | y_true = y_true * weights 27 | 28 | cross_ent = K.categorical_crossentropy(y_pred, y_true) 29 | cross_ent = K.mean(cross_ent, axis=-1) 30 | 31 | return cross_ent 32 | 33 | 34 | # getting the number of GPUs 35 | def get_available_gpus(): 36 | local_device_protos = device_lib.list_local_devices() 37 | return [x.name for x in local_device_protos if x.device_type == 'GPU'] 38 | 39 | 40 | # getting the number of CPUs 41 | def get_available_cpus(): 42 | return multiprocessing.cpu_count() 43 | 44 | 45 | def draw_str(dst, target, s): 46 | x, y = target 47 | cv.putText(dst, s, (x + 1, y + 1), cv.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), thickness=2, lineType=cv.LINE_AA) 48 | cv.putText(dst, s, (x, y), cv.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv.LINE_AA) 49 | 50 | 51 | def get_image(name): 52 | image_path = os.path.join('data', name) 53 | image_path = os.path.join(image_path, folder_rgb_image) 54 | image_name = [f for f in os.listdir(image_path) if f.endswith('.jpg')][0] 55 | image_path = os.path.join(image_path, image_name) 56 | image = cv.imread(image_path) 57 | return image 58 | 59 | 60 | def get_category(id): 61 | filename = os.path.join(seg_path, '{}.png'.format(id)) 62 | category = cv.imread(filename, 0) 63 | return category 64 | 65 | 66 | def to_bgr(category): 67 | h, w = category.shape[:2] 68 | ret = np.zeros((h, w, 3), np.float32) 69 | for r in range(h): 70 | for c in range(w): 71 | color_id = category[r, c] 72 | # print("color_id: " + str(color_id)) 73 | ret[r, c, :] = colors[color_id] 74 | ret = ret.astype(np.uint8) 75 | return ret 76 | 77 | 78 | def safe_crop(mat, x, y): 79 | if len(mat.shape) == 2: 80 | ret = np.zeros((crop_size, crop_size), np.float32) 81 | interpolation = cv.INTER_NEAREST 82 | else: 83 | ret = np.zeros((crop_size, crop_size, 3), np.float32) 84 | interpolation = cv.INTER_CUBIC 85 | crop = mat[y:y + crop_size, x:x + crop_size] 86 | h, w = crop.shape[:2] 87 | ret[0:h, 0:w] = crop 88 | if crop_size != (img_rows, img_cols): 89 | ret = cv.resize(ret, dsize=(img_rows, img_cols), interpolation=interpolation) 90 | ret = ret.astype(np.uint8) 91 | return ret 92 | 93 | 94 | def random_crop(image, category): 95 | height, width = image.shape[:2] 96 | x = random.randint(0, max(0, width - crop_size)) 97 | y = random.randint(0, max(0, height - crop_size)) 98 | image = safe_crop(image, x, y) 99 | category = safe_crop(category, x, y) 100 | return image, category 101 | -------------------------------------------------------------------------------- /valid_ids.txt: -------------------------------------------------------------------------------- 1 | 3234 2 | 2638 3 | 7475 4 | 2339 5 | 142 6 | 2756 7 | 5364 8 | 7359 9 | 5575 10 | 2358 11 | 2528 12 | 6824 13 | 3104 14 | 253 15 | 70 16 | 3358 17 | 2199 18 | 243 19 | 535 20 | 3992 21 | 3944 22 | 2116 23 | 4925 24 | 3176 25 | 6863 26 | 645 27 | 5432 28 | 4232 29 | 3882 30 | 5333 31 | 2800 32 | 6559 33 | 579 34 | 3704 35 | 4460 36 | 77 37 | 7881 38 | 1579 39 | 5389 40 | 3900 41 | 1695 42 | 1642 43 | 5302 44 | 6939 45 | 150 46 | 5303 47 | 5104 48 | 1046 49 | 4024 50 | 862 51 | 2416 52 | 2417 53 | 2014 54 | 4370 55 | 6050 56 | 5819 57 | 6632 58 | 2153 59 | 4181 60 | 1394 61 | 1996 62 | 1745 63 | 6828 64 | 1864 65 | 1801 66 | 6998 67 | 7020 68 | 7323 69 | 2786 70 | 1993 71 | 284 72 | 1692 73 | 1386 74 | 7283 75 | 515 76 | 2036 77 | 7843 78 | 573 79 | 6457 80 | 3421 81 | 5505 82 | 2734 83 | 5970 84 | 8250 85 | 2238 86 | 6242 87 | 5701 88 | 5376 89 | 4706 90 | 4457 91 | 2093 92 | 4683 93 | 4019 94 | 207 95 | 3590 96 | 879 97 | 7313 98 | 2705 99 | 298 100 | 3378 101 | 6847 102 | 924 103 | 2844 104 | 7615 105 | 4659 106 | 6603 107 | 4597 108 | 1228 109 | 4387 110 | 4715 111 | 1190 112 | 1863 113 | 1038 114 | 8088 115 | 2030 116 | 2475 117 | 6844 118 | 5857 119 | 7240 120 | 4441 121 | 5325 122 | 1954 123 | 2260 124 | 5111 125 | 605 126 | 7173 127 | 2637 128 | 860 129 | 2613 130 | 2783 131 | 2096 132 | 2450 133 | 7702 134 | 384 135 | 3958 136 | 2998 137 | 1239 138 | 4707 139 | 4239 140 | 3459 141 | 3894 142 | 1533 143 | 1037 144 | 4396 145 | 2499 146 | 3296 147 | 5420 148 | 6539 149 | 8177 150 | 7258 151 | 4972 152 | 6779 153 | 1562 154 | 2222 155 | 8188 156 | 5354 157 | 3863 158 | 7720 159 | 599 160 | 997 161 | 3427 162 | 5673 163 | 1443 164 | 2197 165 | 5623 166 | 7279 167 | 1165 168 | 4817 169 | 3377 170 | 4197 171 | 5330 172 | 5368 173 | 7411 174 | 2641 175 | 7378 176 | 4532 177 | 1212 178 | 3125 179 | 7915 180 | 3588 181 | 5649 182 | 3997 183 | 1737 184 | 5256 185 | 614 186 | 4665 187 | 2033 188 | 7776 189 | 1141 190 | 4180 191 | 103 192 | 7636 193 | 1223 194 | 3802 195 | 4662 196 | 3497 197 | 3861 198 | 1537 199 | 5712 200 | 6350 201 | 2794 202 | 5572 203 | 3409 204 | 6614 205 | 7753 206 | 4753 207 | 4914 208 | 7927 209 | 8068 210 | 1889 211 | 8121 212 | 4072 213 | 1712 214 | 3664 215 | 4534 216 | 92 217 | 6211 218 | 8030 219 | 3257 220 | 2554 221 | 503 222 | 3400 223 | 2874 224 | 2738 225 | 1040 226 | 1155 227 | 2779 228 | 6020 229 | 334 230 | 4054 231 | 5648 232 | 1328 233 | 1710 234 | 1788 235 | 4425 236 | 6671 237 | 1454 238 | 4330 239 | 2733 240 | 700 241 | 2969 242 | 8126 243 | 8194 244 | 5517 245 | 818 246 | 7249 247 | 5626 248 | 8172 249 | 902 250 | 1765 251 | 2491 252 | 3423 253 | 4292 254 | 6806 255 | 1966 256 | 6871 257 | 711 258 | 8021 259 | 5997 260 | 4938 261 | 4855 262 | 1519 263 | 2228 264 | 2862 265 | 6636 266 | 560 267 | 6620 268 | 2191 269 | 4741 270 | 5353 271 | 6624 272 | 5899 273 | 1081 274 | 5777 275 | 4879 276 | 123 277 | 6860 278 | 7110 279 | 2045 280 | 4315 281 | 5835 282 | 8173 283 | 8103 284 | 5720 285 | 5679 286 | 5841 287 | 1587 288 | 2816 289 | 3755 290 | 5032 291 | 1379 292 | 3942 293 | 4517 294 | 6946 295 | 497 296 | 913 297 | 1127 298 | 3720 299 | 7091 300 | 5799 301 | 5643 302 | 8042 303 | 2481 304 | 6376 305 | 6760 306 | 931 307 | 7831 308 | 4875 309 | 2859 310 | 3954 311 | 170 312 | 7393 313 | 3171 314 | 5940 315 | 1785 316 | 5476 317 | 6869 318 | 607 319 | 661 320 | 4337 321 | 5811 322 | 6194 323 | 878 324 | 3391 325 | 3249 326 | 6619 327 | 6690 328 | 8010 329 | 2642 330 | 7218 331 | 6820 332 | 1882 333 | 3892 334 | 3020 335 | 2337 336 | 7329 337 | 6672 338 | 5600 339 | 5914 340 | 4891 341 | 8262 342 | 93 343 | 7066 344 | 8075 345 | 3533 346 | 73 347 | 358 348 | 5089 349 | 6172 350 | 1546 351 | 7874 352 | 2990 353 | 3901 354 | 504 355 | 7488 356 | 6249 357 | 4795 358 | 7951 359 | 4098 360 | 1398 361 | 5069 362 | 313 363 | 2426 364 | 7944 365 | 1152 366 | 2995 367 | 4953 368 | 814 369 | 4063 370 | 1166 371 | 3902 372 | 6769 373 | 44 374 | 7790 375 | 5373 376 | 617 377 | 8213 378 | 7478 379 | 5151 380 | 2229 381 | 1505 382 | 4506 383 | 3734 384 | 6644 385 | 5578 386 | 6543 387 | 3803 388 | 6234 389 | 5557 390 | 5641 391 | 3659 392 | 7832 393 | 1342 394 | 4746 395 | 6600 396 | 6947 397 | 326 398 | 722 399 | 5045 400 | 3560 401 | 5494 402 | 1953 403 | 1887 404 | 1654 405 | 7217 406 | 4786 407 | 5716 408 | 758 409 | 1310 410 | 1267 411 | 1611 412 | 6181 413 | 4808 414 | 5090 415 | 3307 416 | 1316 417 | 589 418 | 8008 419 | 99 420 | 116 421 | 328 422 | 4025 423 | 3872 424 | 21 425 | 3150 426 | 7402 427 | 7871 428 | 5590 429 | 7851 430 | 3064 431 | 807 432 | 757 433 | 6178 434 | 6699 435 | 1407 436 | 6176 437 | 8105 438 | 7527 439 | 3563 440 | 1019 441 | 4117 442 | 869 443 | 1920 444 | 8241 445 | 8258 446 | 1065 447 | 5735 448 | 7239 449 | 4812 450 | 6678 451 | 2777 452 | 3464 453 | 4297 454 | 7310 455 | 2186 456 | 102 457 | 1999 458 | 3828 459 | 337 460 | 1235 461 | 972 462 | 2884 463 | 1452 464 | 6669 465 | 2474 466 | 1391 467 | 176 468 | 2407 469 | 5233 470 | 5783 471 | 1689 472 | 5821 473 | 7042 474 | 6895 475 | 2573 476 | 1464 477 | 1560 478 | 7869 479 | 3244 480 | 4663 481 | 6328 482 | 4274 483 | 867 484 | 2563 485 | 137 486 | 61 487 | 2423 488 | 7687 489 | 2558 490 | 2362 491 | 5903 492 | 8212 493 | 4322 494 | 2289 495 | 6713 496 | 1200 497 | 6232 498 | 160 499 | 1140 500 | 4562 501 | 2943 502 | 5421 503 | 6594 504 | 7077 505 | 3672 506 | 8099 507 | 5371 508 | 1641 509 | 7212 510 | 3197 511 | 3144 512 | 4883 513 | 5015 514 | 782 515 | 6269 516 | 250 517 | 4480 518 | 190 519 | 3864 520 | 963 521 | 7070 522 | 7792 523 | 6719 524 | 429 525 | 1314 526 | 5508 527 | 3575 528 | 210 529 | 3743 530 | 3214 531 | 2131 532 | 4536 533 | 2378 534 | 5591 535 | 151 536 | 2540 537 | 236 538 | 7880 539 | 2127 540 | 3347 541 | 1831 542 | 2857 543 | 1697 544 | 7991 545 | 7536 546 | 4050 547 | 2808 548 | 2640 549 | 8176 550 | 7904 551 | 1496 552 | 122 553 | 2114 554 | 254 555 | 3775 556 | 7396 557 | 3856 558 | 5852 559 | 3967 560 | 4014 561 | 4361 562 | 7613 563 | 2655 564 | 2443 565 | 1233 566 | 4317 567 | 3833 568 | 8178 569 | 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| 7463 755 | 2061 756 | 2983 757 | 4541 758 | 84 759 | 690 760 | 4143 761 | 4627 762 | 1499 763 | 6284 764 | 7087 765 | 4236 766 | 3327 767 | 4453 768 | 5941 769 | 68 770 | 1959 771 | 2031 772 | 2081 773 | 1713 774 | 864 775 | 7361 776 | 3013 777 | 2356 778 | 6925 779 | 3939 780 | 4820 781 | 964 782 | 751 783 | 53 784 | 3325 785 | 3538 786 | 822 787 | 559 788 | 4362 789 | 5005 790 | 3753 791 | 7533 792 | 2904 793 | 1175 794 | 7745 795 | 5985 796 | 5520 797 | 7146 798 | 7610 799 | 4680 800 | 7011 801 | 4626 802 | 4068 803 | 6153 804 | 5826 805 | 7598 806 | 1757 807 | 2569 808 | 6919 809 | 7097 810 | 3203 811 | 6247 812 | 3441 813 | 7111 814 | 1103 815 | 7779 816 | 1693 817 | 3142 818 | 4798 819 | 4252 820 | 138 821 | 7456 822 | 4437 823 | 1172 824 | 7443 825 | 6499 826 | 5324 827 | 3215 828 | 4787 829 | 7060 830 | 6914 831 | 6723 832 | 6859 833 | 5016 834 | 6221 835 | 2386 836 | 4008 837 | 1720 838 | 4716 839 | 1179 840 | 5008 841 | 1666 842 | 6305 843 | 732 844 | 7076 845 | 6057 846 | 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| 3620 1113 | 753 1114 | 1994 1115 | 7326 1116 | 401 1117 | 4802 1118 | 31 1119 | 1142 1120 | 1178 1121 | 7524 1122 | 4618 1123 | 1580 1124 | 552 1125 | 7102 1126 | 2355 1127 | 5429 1128 | 136 1129 | 2004 1130 | 2681 1131 | 7299 1132 | 2272 1133 | 6163 1134 | 65 1135 | 3159 1136 | 4832 1137 | 7473 1138 | 290 1139 | 4545 1140 | 2721 1141 | 6372 1142 | 7200 1143 | 5946 1144 | 7428 1145 | 1123 1146 | 5661 1147 | 7677 1148 | 8175 1149 | 6086 1150 | 889 1151 | 1414 1152 | 1735 1153 | 3557 1154 | 438 1155 | 2812 1156 | 3410 1157 | 595 1158 | 3700 1159 | 3806 1160 | 2516 1161 | 422 1162 | 7535 1163 | 5876 1164 | 911 1165 | 1542 1166 | 1487 1167 | 2477 1168 | 6997 1169 | 6780 1170 | 6611 1171 | 2080 1172 | 2216 1173 | 2335 1174 | 6426 1175 | 5693 1176 | 7005 1177 | 5920 1178 | 4302 1179 | 3762 1180 | 3633 1181 | 4433 1182 | 3339 1183 | 7802 1184 | 3316 1185 | 8187 1186 | 7291 1187 | 1055 1188 | 2929 1189 | 7479 1190 | 4156 1191 | 3626 1192 | 7160 1193 | 1652 1194 | 2165 1195 | 492 1196 | 1384 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6572 1703 | 4133 1704 | 952 1705 | 6109 1706 | 5363 1707 | 682 1708 | 4604 1709 | 6714 1710 | 6334 1711 | 1042 1712 | 6519 1713 | 6297 1714 | 3661 1715 | 2908 1716 | 1219 1717 | 2438 1718 | 3906 1719 | 5448 1720 | 106 1721 | 1241 1722 | 111 1723 | 6949 1724 | 7395 1725 | 3102 1726 | 4129 1727 | 7504 1728 | 7650 1729 | 6855 1730 | 4075 1731 | 8204 1732 | 5604 1733 | 6849 1734 | 1819 1735 | 6958 1736 | 5440 1737 | 6763 1738 | 4388 1739 | 3718 1740 | 7268 1741 | 2780 1742 | 7604 1743 | 2249 1744 | 5438 1745 | 2587 1746 | 3784 1747 | 8243 1748 | 5486 1749 | 5232 1750 | 4112 1751 | 5401 1752 | 18 1753 | 7657 1754 | 7556 1755 | 6520 1756 | 6134 1757 | 34 1758 | 810 1759 | 4807 1760 | 2370 1761 | 2792 1762 | 46 1763 | 5885 1764 | 5879 1765 | 5858 1766 | 2056 1767 | 5295 1768 | 844 1769 | 5101 1770 | 6222 1771 | 2979 1772 | 6003 1773 | 8001 1774 | 2128 1775 | 2239 1776 | 1884 1777 | 1779 1778 | 1307 1779 | 258 1780 | 5318 1781 | 4990 1782 | 3621 1783 | 3869 1784 | 5977 1785 | 6716 1786 | 7280 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| 7416 1957 | 7771 1958 | 7683 1959 | 3617 1960 | 7129 1961 | 2755 1962 | 7952 1963 | 5987 1964 | 550 1965 | 3181 1966 | 1466 1967 | 6047 1968 | 2106 1969 | 6787 1970 | 7814 1971 | 7503 1972 | 2283 1973 | 2445 1974 | 3519 1975 | 486 1976 | 2839 1977 | 4165 1978 | 1600 1979 | 6170 1980 | 4859 1981 | 4720 1982 | 8265 1983 | 3887 1984 | 3981 1985 | 3654 1986 | 5725 1987 | 2440 1988 | 3884 1989 | 3571 1990 | 5953 1991 | 2026 1992 | 1616 1993 | 6131 1994 | 3667 1995 | 1691 1996 | 1275 1997 | 4183 1998 | 7900 1999 | 5394 2000 | 537 2001 | 7778 2002 | 1589 2003 | 49 2004 | 7651 2005 | 6902 2006 | 5361 2007 | 1312 2008 | 7338 2009 | 2405 2010 | 7196 2011 | 7888 2012 | 4093 2013 | 5964 2014 | 5002 2015 | 713 2016 | 2086 2017 | 56 2018 | 6721 2019 | 830 2020 | 5109 2021 | 1596 2022 | 5328 2023 | 7573 2024 | 3609 2025 | 398 2026 | 7587 2027 | 3663 2028 | 5425 2029 | 2267 2030 | 5086 2031 | 5676 2032 | 3569 2033 | 6511 2034 | 2366 2035 | 2340 2036 | 3294 2037 | 4365 2038 | 3511 2039 | 6616 2040 | 5497 2041 | 7115 2042 | 7656 2043 | 1385 2044 | 3825 2045 | 3615 2046 | 1909 2047 | 2270 2048 | 4147 2049 | 3164 2050 | 3961 2051 | 7103 2052 | 259 2053 | 8224 2054 | 3793 2055 | 411 2056 | 6507 2057 | 6765 2058 | 5831 2059 | 397 2060 | 4191 2061 | 6992 2062 | 7600 2063 | 2469 2064 | 2255 2065 | 5615 2066 | 2977 2067 | 186 -------------------------------------------------------------------------------- /vgg16.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import keras.backend as K 4 | from keras.layers import Conv2D, ZeroPadding2D, MaxPooling2D 5 | from keras.layers import Dense, Dropout, Flatten 6 | from keras.models import Sequential 7 | 8 | 9 | def vgg16_model(img_rows, img_cols, channel=3): 10 | model = Sequential() 11 | # Encoder 12 | model.add(ZeroPadding2D((1, 1), input_shape=(img_rows, img_cols, channel), name='input')) 13 | model.add(Conv2D(64, (3, 3), activation='relu', name='conv1_1')) 14 | model.add(ZeroPadding2D((1, 1))) 15 | model.add(Conv2D(64, (3, 3), activation='relu', name='conv1_2')) 16 | model.add(MaxPooling2D((2, 2), strides=(2, 2))) 17 | 18 | model.add(ZeroPadding2D((1, 1))) 19 | model.add(Conv2D(128, (3, 3), activation='relu', name='conv2_1')) 20 | model.add(ZeroPadding2D((1, 1))) 21 | model.add(Conv2D(128, (3, 3), activation='relu', name='conv2_2')) 22 | model.add(MaxPooling2D((2, 2), strides=(2, 2))) 23 | 24 | model.add(ZeroPadding2D((1, 1))) 25 | model.add(Conv2D(256, (3, 3), activation='relu', name='conv3_1')) 26 | model.add(ZeroPadding2D((1, 1))) 27 | model.add(Conv2D(256, (3, 3), activation='relu', name='conv3_2')) 28 | model.add(ZeroPadding2D((1, 1))) 29 | model.add(Conv2D(256, (3, 3), activation='relu', name='conv3_3')) 30 | model.add(MaxPooling2D((2, 2), strides=(2, 2))) 31 | 32 | model.add(ZeroPadding2D((1, 1))) 33 | model.add(Conv2D(512, (3, 3), activation='relu', name='conv4_1')) 34 | model.add(ZeroPadding2D((1, 1))) 35 | model.add(Conv2D(512, (3, 3), activation='relu', name='conv4_2')) 36 | model.add(ZeroPadding2D((1, 1))) 37 | model.add(Conv2D(512, (3, 3), activation='relu', name='conv4_3')) 38 | model.add(MaxPooling2D((2, 2), strides=(2, 2))) 39 | 40 | model.add(ZeroPadding2D((1, 1))) 41 | model.add(Conv2D(512, (3, 3), activation='relu', name='conv5_1')) 42 | model.add(ZeroPadding2D((1, 1))) 43 | model.add(Conv2D(512, (3, 3), activation='relu', name='conv5_2')) 44 | model.add(ZeroPadding2D((1, 1))) 45 | model.add(Conv2D(512, (3, 3), activation='relu', name='conv5_3')) 46 | model.add(MaxPooling2D((2, 2), strides=(2, 2))) 47 | 48 | # Add Fully Connected Layer 49 | model.add(Flatten(name='flatten')) 50 | model.add(Dense(4096, activation='relu', name='dense1')) 51 | model.add(Dropout(0.5)) 52 | model.add(Dense(4096, activation='relu', name='dense2')) 53 | model.add(Dropout(0.5)) 54 | model.add(Dense(1000, activation='softmax', name='softmax')) 55 | 56 | # Loads ImageNet pre-trained data 57 | weights_path = 'models/vgg16_weights_tf_dim_ordering_tf_kernels.h5' 58 | model.load_weights(weights_path) 59 | 60 | return model 61 | 62 | 63 | if __name__ == '__main__': 64 | model = vgg16_model(224, 224, 3) 65 | # input_layer = model.get_layer('input') 66 | print(model.summary()) 67 | 68 | K.clear_session() 69 | --------------------------------------------------------------------------------