├── .gitignore ├── Data ├── Checkpoints │ ├── best_weights.h5 │ └── logs │ │ ├── events.out.tfevents.1497537626.MacBook-Pro.local │ │ ├── events.out.tfevents.1497537853.MacBook-Pro.local │ │ ├── events.out.tfevents.1497537913.MacBook-Pro.local │ │ ├── events.out.tfevents.1497537965.MacBook-Pro.local │ │ ├── events.out.tfevents.1497538001.MacBook-Pro.local │ │ └── events.out.tfevents.1497538083.MacBook-Pro.local ├── Model │ ├── model.json │ └── weights.h5 ├── Train_Data │ ├── input │ │ ├── cat.0.jpg │ │ ├── cat.1.jpg │ │ ├── cat.10.jpg │ │ ├── cat.11.jpg │ │ ├── cat.12.jpg │ │ ├── cat.13.jpg │ │ ├── cat.14.jpg │ │ ├── cat.15.jpg │ │ ├── cat.16.jpg │ │ ├── cat.17.jpg │ │ ├── cat.18.jpg │ │ ├── cat.19.jpg │ │ ├── cat.2.jpg │ │ ├── cat.20.jpg │ │ ├── cat.21.jpg │ │ ├── cat.22.jpg │ │ ├── cat.23.jpg │ │ ├── cat.24.jpg │ │ ├── cat.25.jpg │ │ ├── cat.26.jpg │ │ ├── cat.27.jpg │ │ ├── cat.28.jpg │ │ ├── cat.29.jpg │ │ ├── cat.3.jpg │ │ ├── cat.30.jpg │ │ ├── cat.31.jpg │ │ ├── cat.32.jpg │ │ ├── cat.33.jpg │ │ ├── cat.34.jpg │ │ ├── cat.35.jpg │ │ ├── cat.36.jpg │ │ ├── cat.37.jpg │ │ ├── cat.38.jpg │ │ ├── cat.39.jpg │ │ ├── cat.4.jpg │ │ ├── cat.40.jpg │ │ ├── cat.41.jpg │ │ ├── cat.42.jpg │ │ ├── cat.43.jpg │ │ ├── cat.44.jpg │ │ ├── cat.45.jpg │ │ ├── cat.46.jpg │ │ ├── cat.47.jpg │ │ ├── cat.48.jpg │ │ ├── cat.49.jpg │ │ ├── cat.5.jpg │ │ ├── cat.50.jpg │ │ ├── cat.51.jpg │ │ ├── cat.52.jpg │ │ ├── cat.53.jpg │ │ ├── cat.54.jpg │ │ ├── cat.55.jpg │ │ ├── cat.56.jpg │ │ ├── cat.57.jpg │ │ ├── cat.58.jpg │ │ ├── cat.59.jpg │ │ ├── cat.6.jpg │ │ ├── cat.60.jpg │ │ ├── cat.61.jpg │ │ ├── cat.62.jpg │ │ ├── cat.63.jpg │ │ ├── cat.64.jpg │ │ ├── cat.65.jpg │ │ ├── cat.66.jpg │ │ ├── cat.67.jpg │ │ ├── cat.68.jpg │ │ ├── cat.69.jpg │ │ ├── cat.7.jpg │ │ ├── cat.70.jpg │ │ ├── cat.71.jpg │ │ ├── cat.72.jpg │ │ ├── cat.73.jpg │ │ ├── cat.74.jpg │ │ ├── cat.75.jpg │ │ ├── cat.76.jpg │ │ ├── cat.77.jpg │ │ ├── cat.78.jpg │ │ ├── cat.79.jpg │ │ ├── cat.8.jpg │ │ ├── cat.80.jpg │ │ └── cat.9.jpg │ └── mask │ │ ├── mask_cat.0.jpg │ │ ├── mask_cat.1.jpg │ │ ├── mask_cat.10.jpg │ │ ├── mask_cat.11.jpg │ │ ├── mask_cat.12.jpg │ │ ├── mask_cat.13.jpg │ │ ├── mask_cat.14.jpg │ │ ├── mask_cat.15.jpg │ │ ├── mask_cat.16.jpg │ │ ├── mask_cat.17.jpg │ │ ├── mask_cat.18.jpg │ │ ├── mask_cat.19.jpg │ │ ├── mask_cat.2.jpg │ │ ├── mask_cat.20.jpg │ │ ├── mask_cat.21.jpg │ │ ├── mask_cat.22.jpg │ │ ├── mask_cat.23.jpg │ │ ├── mask_cat.24.jpg │ │ ├── mask_cat.25.jpg │ │ ├── mask_cat.26.jpg │ │ ├── mask_cat.27.jpg │ │ ├── mask_cat.28.jpg │ │ ├── mask_cat.29.jpg │ │ ├── mask_cat.3.jpg │ │ ├── mask_cat.30.jpg │ │ ├── mask_cat.31.jpg │ │ ├── mask_cat.32.jpg │ │ ├── mask_cat.33.jpg │ │ ├── mask_cat.34.jpg │ │ ├── mask_cat.35.jpg │ │ ├── mask_cat.36.jpg │ │ ├── mask_cat.37.jpg │ │ ├── mask_cat.38.jpg │ │ ├── mask_cat.39.jpg │ │ ├── mask_cat.4.jpg │ │ ├── mask_cat.40.jpg │ │ ├── mask_cat.41.jpg │ │ ├── mask_cat.42.jpg │ │ ├── mask_cat.43.jpg │ │ ├── mask_cat.44.jpg │ │ ├── mask_cat.45.jpg │ │ ├── mask_cat.46.jpg │ │ ├── mask_cat.47.jpg │ │ ├── mask_cat.48.jpg │ │ ├── mask_cat.49.jpg │ │ ├── mask_cat.5.jpg │ │ ├── mask_cat.50.jpg │ │ ├── mask_cat.51.jpg │ │ ├── mask_cat.52.jpg │ │ ├── mask_cat.53.jpg │ │ ├── mask_cat.54.jpg │ │ ├── mask_cat.55.jpg │ │ ├── mask_cat.56.jpg │ │ ├── mask_cat.57.jpg │ │ ├── mask_cat.58.jpg │ │ ├── mask_cat.59.jpg │ │ ├── mask_cat.6.jpg │ │ ├── mask_cat.60.jpg │ │ ├── mask_cat.61.jpg │ │ ├── mask_cat.62.jpg │ │ ├── mask_cat.63.jpg │ │ ├── mask_cat.64.jpg │ │ ├── mask_cat.65.jpg │ │ ├── mask_cat.66.jpg │ │ ├── mask_cat.67.jpg │ │ ├── mask_cat.68.jpg │ │ ├── mask_cat.69.jpg │ │ ├── mask_cat.7.jpg │ │ ├── mask_cat.70.jpg │ │ ├── mask_cat.71.jpg │ │ ├── mask_cat.72.jpg │ │ ├── mask_cat.73.jpg │ │ ├── mask_cat.74.jpg │ │ ├── mask_cat.75.jpg │ │ ├── mask_cat.76.jpg │ │ ├── mask_cat.77.jpg │ │ ├── mask_cat.78.jpg │ │ ├── mask_cat.79.jpg │ │ ├── mask_cat.8.jpg │ │ ├── mask_cat.80.jpg │ │ └── mask_cat.9.jpg └── npy_train_data │ ├── X.npy │ └── Y.npy ├── LICENSE ├── README.md ├── get_dataset.py ├── get_model.py ├── predict.py ├── requirements.txt ├── segmentated.jpg ├── test.jpg └── train.py /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | .DS_Store 3 | -------------------------------------------------------------------------------- /Data/Checkpoints/best_weights.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ardamavi/Cat-Segmentation/38d08c4527156a013808019d5e76c6ebf165a9e8/Data/Checkpoints/best_weights.h5 -------------------------------------------------------------------------------- /Data/Checkpoints/logs/events.out.tfevents.1497537626.MacBook-Pro.local: -------------------------------------------------------------------------------- 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"VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"name": "activation_1", "class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "activation": "relu"}, "inbound_nodes": [[["conv2d_1", 0, 0, {}]]]}, {"name": "conv2d_2", "class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activation_1", 0, 0, {}]]]}, {"name": "activation_2", "class_name": "Activation", "config": {"name": "activation_2", "trainable": true, "activation": "relu"}, "inbound_nodes": [[["conv2d_2", 0, 0, {}]]]}, {"name": "conv2d_transpose_1", "class_name": "Conv2DTranspose", "config": {"name": "conv2d_transpose_1", "trainable": true, "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": 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The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright 2017 Arda Mavi 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Cat-Segmentation 2 | ### By Arda Mavi 3 | 4 | Cat segmentation with deep learning.
5 | Database created by myself. 6 | 7 | ### Segmentation Example: 8 | ||| 9 | |:-:|:-:| 10 | | Orijinal | Segmented | 11 | 12 | 13 | ### Using Predict Command: 14 | `python3 predict.py ` 15 | 16 | ### Model Training: 17 | `python3 train.py` 18 | 19 | ### Using TensorBoard: 20 | `tensorboard --logdir=Data/Checkpoints/logs` 21 | 22 | ### Model Architecture: 23 | - Input Data 24 | Shape: 64x64x3 25 | 26 | - Convolutional Layer 27 | 32 filter 28 | Filter shape: 3x3 29 | Strides: 1x1 30 | 31 | - Activation 32 | Function: ReLu 33 | 34 | - Convolutional Layer 35 | 64 filter 36 | Filter shape: 3x3 37 | Strides: 1x1 38 | 39 | - Activation 40 | Function: ReLu 41 | 42 | - Transpose Convolutional Layer 43 | 64 filter 44 | Filter shape: 3x3 45 | Strides: 1x1 46 | 47 | - Activation 48 | Function: ReLu 49 | 50 | - Merge Layer 51 | 52 | - Transpose Convolutional Layer 53 | 1 filter 54 | Filter shape: 3x3 55 | Strides: 1x1 56 | 57 | - Activation 58 | Function: Sigmoid 59 | 60 | ##### Optimizer: Adadelta 61 | ##### Loss: Dice Coefficient 62 | 63 | ### Important Notes: 64 | - Used Python Version: 3.6.0 65 | 66 | - Install necessary modules with `sudo pip3 install -r requirements.txt` command. 67 | 68 | - We work on 64x64 image also if you use bigger, program will automatically return to 64x64. 69 | -------------------------------------------------------------------------------- /get_dataset.py: -------------------------------------------------------------------------------- 1 | # Arda Mavi 2 | import os 3 | import numpy as np 4 | from os import listdir 5 | from matplotlib import pyplot as plt 6 | from scipy.misc import imread, imresize, toimage, imsave 7 | from sklearn.model_selection import train_test_split 8 | 9 | def get_img(data_path): 10 | # Getting image array from path: 11 | img = imread(data_path) 12 | img = imresize(img, (64, 64)) 13 | return img 14 | 15 | def save_img(img, name='segmentated.jpg'): 16 | imsave(name, img.reshape(64, 64)) 17 | 18 | def get_dataset(dataset_path='Data/Train_Data'): 19 | # Getting all data from data path: 20 | try: 21 | X = np.load('Data/npy_train_data/X.npy') 22 | Y = np.load('Data/npy_train_data/Y.npy') 23 | except: 24 | inputs_path = dataset_path+'/input' 25 | images = listdir(inputs_path) # Geting images 26 | X = [] 27 | Y = [] 28 | for img in images: 29 | img_path = inputs_path+'/'+img 30 | 31 | x_img = get_img(img_path).astype('float32').reshape(64, 64, 3) 32 | x_img /= 255. 33 | 34 | y_img = get_img(img_path.replace('input/', 'mask/mask_')).astype('float32').reshape(64, 64, 1) 35 | y_img /= 255. 36 | 37 | X.append(x_img) 38 | Y.append(y_img) 39 | X = np.array(X) 40 | Y = np.array(Y) 41 | # Create dateset: 42 | if not os.path.exists('Data/npy_train_data/'): 43 | os.makedirs('Data/npy_train_data/') 44 | np.save('Data/npy_train_data/X.npy', X) 45 | np.save('Data/npy_train_data/Y.npy', Y) 46 | X, X_test, Y, Y_test = train_test_split(X, Y, test_size=0.1, random_state=42) 47 | return X, X_test, Y, Y_test 48 | -------------------------------------------------------------------------------- /get_model.py: -------------------------------------------------------------------------------- 1 | # Arda Mavi 2 | import os 3 | import numpy as np 4 | from keras.models import Model 5 | from keras import backend as K 6 | from keras.layers import Input, Conv2D, Activation, MaxPooling2D, Conv2DTranspose, concatenate 7 | 8 | def dice_coef(y_true, y_pred): 9 | smooth = 1. 10 | y_true_f = K.flatten(y_true) 11 | y_pred_f = K.flatten(y_pred) 12 | intersection = K.sum(y_true_f * y_pred_f) 13 | return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth) 14 | 15 | def dice_coef_loss(y_true, y_pred): 16 | return -dice_coef(y_true, y_pred) 17 | 18 | def save_model(model): 19 | if not os.path.exists('Data/Model/'): 20 | os.makedirs('Data/Model/') 21 | model_json = model.to_json() 22 | with open("Data/Model/model.json", "w") as model_file: 23 | model_file.write(model_json) 24 | # serialize weights to HDF5 25 | model.save_weights("Data/Model/weights.h5") 26 | print('Model and weights saved') 27 | return 28 | 29 | def get_model(): 30 | 31 | inputs = Input(shape=(64, 64, 3)) 32 | 33 | conv_1 = Conv2D(1, (3, 3), strides=(1, 1), padding='same')(inputs) 34 | act_1 = Activation('relu')(conv_1) 35 | 36 | conv_2 = Conv2D(64, (3, 3), strides=(1, 1), padding='same')(act_1) 37 | act_2 = Activation('relu')(conv_2) 38 | 39 | deconv_1 = Conv2DTranspose(64, (3, 3), strides=(1, 1), padding='same')(act_2) 40 | act_3 = Activation('relu')(deconv_1) 41 | 42 | merge_1 = concatenate([act_3, act_1], axis=3) 43 | 44 | deconv_2 = Conv2DTranspose(1, (3, 3), strides=(1, 1), padding='same')(merge_1) 45 | act_4 = Activation('relu')(deconv_2) 46 | 47 | model = Model(inputs=[inputs], outputs=[act_4]) 48 | 49 | model.compile(optimizer='adadelta', loss=dice_coef_loss, metrics=[dice_coef]) 50 | 51 | return model 52 | 53 | if __name__ == '__main__': 54 | save_model(get_model()) 55 | -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | # Arda Mavi 2 | import sys 3 | import numpy as np 4 | from get_dataset import get_img, save_img 5 | from keras.models import model_from_json 6 | 7 | def predict(model, X): 8 | X = X.reshape(1, 64, 64, 3) 9 | Y = model.predict(X).reshape(64, 64, 1) 10 | Y *= 255. 11 | return Y 12 | 13 | def main(img_dir): 14 | img = get_img(img_dir).astype('float32') 15 | img /= 255. 16 | # Getting model: 17 | model_file = open('Data/Model/model.json', 'r') 18 | model = model_file.read() 19 | model_file.close() 20 | model = model_from_json(model) 21 | # Getting weights 22 | model.load_weights("Data/Model/weights.h5") 23 | Y = predict(model, img) 24 | name = 'segmentated.jpg' 25 | save_img(Y, name) 26 | print('Segmentated image saved as '+name) 27 | 28 | if __name__ == '__main__': 29 | main(sys.argv[1]) 30 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | scikit-learn 3 | scikit-image 4 | tensorflow 5 | keras 6 | h5py 7 | -------------------------------------------------------------------------------- /segmentated.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ardamavi/Cat-Segmentation/38d08c4527156a013808019d5e76c6ebf165a9e8/segmentated.jpg -------------------------------------------------------------------------------- /test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ardamavi/Cat-Segmentation/38d08c4527156a013808019d5e76c6ebf165a9e8/test.jpg -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | # Arda Mavi 2 | import os 3 | from get_dataset import get_dataset 4 | from get_model import get_model, save_model 5 | from keras.callbacks import ModelCheckpoint, TensorBoard 6 | 7 | epochs = 50 8 | batch_size = 5 9 | 10 | def train_model(model, X, X_test, Y, Y_test): 11 | if not os.path.exists('Data/Checkpoints/'): 12 | os.makedirs('Data/Checkpoints/') 13 | checkpoints = [] 14 | checkpoints.append(ModelCheckpoint('Data/Checkpoints/best_weights.h5', monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=True, mode='auto', period=1)) 15 | checkpoints.append(TensorBoard(log_dir='Data/Checkpoints/./logs', histogram_freq=0, write_graph=True, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None)) 16 | 17 | model.fit(X, Y, batch_size=batch_size, epochs=epochs, validation_data=(X_test, Y_test), shuffle=True, callbacks=checkpoints) 18 | 19 | return model 20 | 21 | def main(): 22 | X, X_test, Y, Y_test = get_dataset() 23 | model = get_model() 24 | model = train_model(model, X, X_test, Y, Y_test) 25 | save_model(model) 26 | return model 27 | 28 | if __name__ == '__main__': 29 | main() 30 | --------------------------------------------------------------------------------