├── LICENSE ├── README.md ├── deepClassificationTool.py ├── modelInceptionV3m.py ├── modelResNetM.py ├── modelVGGm.py └── testFunctions.py /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. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. 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Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # deepClassificationTool 2 | Deep Image Classification Tool based on Keras. Tool implements light versions of VGG, ResNet and InceptionV3 for small images. 3 | Tool uses python 3.5. 4 | 5 | **Tool has 3 modes:** 6 | 1) **Training of new deep neural network** (train_flag = True, tune_flag = False). 7 | 2) **Tuning of existing deep neural network** (train_flag = True, tune_flag = True). 8 | 3) **Testing of existing (trained) deep neural network** (train_flag = False, tune_flag = False) or (train_flag = False, tune_flag = True). 9 | 10 | For training and tune mode you need two folders: 11 | 1) Training folder with subfolders - one for each image class 12 | 2) Test folder with subfolders - one for each image class 13 | 14 | Example of folders tree: 15 | 16 | train/ImageClass1 17 | /ImageClass2 18 | /ImageClass3 19 | /ImageClass4 20 | ... 21 | 22 | test/ImageClass1 23 | /ImageClass2 24 | /ImageClass3 25 | /ImageClass4 26 | ... 27 | 28 | Here each subfolder 'ImageClassi' consists set of images of i-th class. 29 | 30 | ***Description of main modules:*** 31 | 32 | ***deepClassificationTool.py*** - main module for training and testing of deep neural network 33 | 34 | ***testFunctions.py*** - functions for testing of trained deep neural network on one image, two images and folder of images (with calculation of recall, precision for each class and accuracy) 35 | 36 | ***modelVGGm.py*** - Light version of VGG for small images - Inspired from VGG, 2014 - VGGm(modified) 37 | 38 | ***modelResNetM.py*** - Light version of ResNet for small images - resNetM (modified) 39 | 40 | # Reference - [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) 41 | 42 | # Reference - [https://github.com/fchollet/keras/blob/master/keras/applications/resnet50.py] 43 | 44 | ***modelInceptionV3m.py*** - Light version of inceptionV3 - inceptionV3m (modified) 45 | 46 | # Reference - [https://github.com/fchollet/keras/blob/master/keras/applications/inception_v3.py] 47 | 48 | # Reference - [Rethinking the Inception Architecture for Computer Vision](http://arxiv.org/abs/1512.00567) 49 | -------------------------------------------------------------------------------- /deepClassificationTool.py: -------------------------------------------------------------------------------- 1 | import keras 2 | from keras.preprocessing.image import ImageDataGenerator 3 | from keras import backend as K 4 | from keras import optimizers 5 | import time 6 | from modelInceptionV3m import inceptionV3m, inceptionV3Only 7 | from modelResNetM import resNetM 8 | from modelVGGm import VGGm 9 | from testFunctions import test_image, test_two_image, test_image_gen, test_path_gen 10 | 11 | # dimensions of our images. 12 | img_width, img_height = 135, 76 13 | 14 | train_data_dir = 'D:/Datasets/copters/train' 15 | validation_data_dir = 'D:/Datasets/copters/test' 16 | 17 | train_tune_data_dir = 'D:/Datasets/copters/train' 18 | validation_tune_data_dir = 'D:/Datasets/copters/test' 19 | 20 | epochs = 30 21 | tune_epochs = 10 22 | batch_size = 16 23 | train_flag = True 24 | tune_flag = False 25 | #weights_path = 'car_5angles_weights_50_30_inceptionv3_wtd.h5'#'cars_direct_1.h5' 26 | weights_path = 'camera_on_off_VGG.h5' 27 | weights_tuned_path = 'camera_on_off_tuned.h5'#'cars_direct_tuned.h5' 28 | 29 | if K.image_data_format() == 'channels_first': 30 | input_shape = (3, img_width, img_height) 31 | else: 32 | input_shape = (img_width, img_height, 3) 33 | 34 | #model = inceptionV3m(input_shape, 5) 35 | # 36 | #model = inceptionV3Only(input_shape, 5) 37 | model = VGGm(input_shape, 5) 38 | #model = resNetM(input_shape, 5) 39 | 40 | 41 | #adam = optimizers.Adam(lr=1e-5) 42 | sgd = optimizers.SGD(lr=0.0001, decay=0.0, momentum=0.9, nesterov=True) 43 | 44 | if(train_flag == True): 45 | if(tune_flag): 46 | model.load_weights(weights_path) 47 | 48 | # Training of neural network 49 | model.compile(loss='categorical_crossentropy', 50 | optimizer=sgd, #adam, #'sgd', 51 | metrics=['accuracy']) 52 | print('Model is compiled\n') 53 | model.summary() 54 | 55 | # this is the augmentation configuration we will use for training 56 | train_datagen = ImageDataGenerator( 57 | rescale=1. / 255, 58 | shear_range=0.2, 59 | zoom_range=0.2, 60 | horizontal_flip=True) 61 | 62 | # this is the augmentation configuration we will use for testing: 63 | # only rescaling 64 | test_datagen = ImageDataGenerator(rescale=1. / 255) 65 | if not tune_flag: 66 | train_generator = train_datagen.flow_from_directory( 67 | train_data_dir, 68 | target_size=(img_width, img_height), 69 | batch_size=batch_size, 70 | class_mode='categorical') 71 | 72 | validation_generator = test_datagen.flow_from_directory( 73 | validation_data_dir, 74 | target_size=(img_width, img_height), 75 | batch_size=batch_size, 76 | class_mode='categorical') 77 | else: 78 | train_generator = train_datagen.flow_from_directory( 79 | train_tune_data_dir, 80 | target_size=(img_width, img_height), 81 | batch_size=batch_size, 82 | class_mode='categorical') 83 | 84 | validation_generator = test_datagen.flow_from_directory( 85 | validation_tune_data_dir, 86 | target_size=(img_width, img_height), 87 | batch_size=batch_size, 88 | class_mode='categorical') 89 | print('Data is generated from folders\n') 90 | 91 | epochs_train = epochs 92 | if tune_flag: 93 | epochs_train = tune_epochs 94 | 95 | start = time.time() 96 | 97 | nb_train_samples = train_generator.samples 98 | nb_validation_samples = validation_generator.samples 99 | 100 | callbacks = [keras.callbacks.ModelCheckpoint( 101 | 'D:/Projects/deepClassificationTool-master/models/camera_on_off_weights.{epoch:02d}-{val_loss:.2f}-{loss:.2f}.hdf5', 102 | verbose=1, 103 | save_weights_only=True)] 104 | 105 | hist = model.fit_generator( 106 | train_generator, 107 | steps_per_epoch=nb_train_samples // batch_size, 108 | epochs=epochs_train, 109 | callbacks=callbacks, 110 | validation_data=validation_generator, 111 | validation_steps=nb_validation_samples // batch_size) 112 | stop = time.time() 113 | sec = stop - start 114 | print("ConvNet is trained! Training time = %.4f sec" % sec, end=' ') 115 | print(hist.history) 116 | if(tune_flag): 117 | model.save_weights(weights_tuned_path) 118 | else: 119 | model.save_weights(weights_path) 120 | print('ConvNet is saved\n') 121 | K.clear_session() 122 | else: 123 | #Testing of neural network 124 | if (tune_flag): 125 | model.load_weights(weights_tuned_path) 126 | else: 127 | model.load_weights(weights_path) 128 | model.compile(loss='categorical_crossentropy', 129 | optimizer=sgd, #adam, #'sgd' 130 | metrics=['accuracy']) 131 | print('Model is compiled\n') 132 | 133 | 134 | test_path_gen('D:/Datasets/copters/train', img_width, img_height, model, 135 | save_e_path='D:/Datasets/copters/errorresults_special_train_vgg', 136 | save_tune_path='D:/Datasets/copters/tune_special_train_vgg', save_errors=True, 137 | clear_tune_path=False) 138 | test_path_gen('D:/Datasets/copters/test', img_width, img_height, model, 139 | save_e_path='D:/Datasets/copters/errorresults_special_vgg', 140 | save_tune_path='D:/Datasets/copters/tune_special_vgg', save_errors=True, clear_tune_path=False) 141 | """ 142 | test_path_gen('D:/Datasets/cars_angles/validation', img_width, img_height, model, 143 | save_e_path='D:/Datasets/cars_angles/errorresults_special_validation_resnet', 144 | save_tune_path='D:/Datasets/cars_angles/tune_special_validation_resnet', save_errors=False, 145 | clear_tune_path=False) 146 | """ 147 | """ 148 | test_image('D:/Datasets/cars_angles/special_test/1_1 (2).JPG', img_width, img_height, model) 149 | test_image('D:/Datasets/cars_angles/special_test/1_2 (2).JPG', img_width, img_height, model) 150 | test_two_image('D:/Datasets/cars_direction/special_test/back/1_0 (2).JPG', 'D:/Datasets/cars_direction/special_test/back/1_3 (2).JPG', img_width, img_height, model) 151 | """ 152 | K.clear_session() -------------------------------------------------------------------------------- /modelInceptionV3m.py: -------------------------------------------------------------------------------- 1 | 2 | from keras.models import Model 3 | from keras import layers 4 | from keras.layers import Conv2D, MaxPooling2D, BatchNormalization,Input, AveragePooling2D, GlobalAveragePooling2D 5 | from keras.layers import Activation, Dropout, Dense 6 | from keras import backend as K 7 | 8 | 9 | # Reference - [Rethinking the Inception Architecture for Computer Vision](http://arxiv.org/abs/1512.00567) 10 | def conv2d_bn(x, 11 | filters, 12 | num_row, 13 | num_col, 14 | padding='same', 15 | strides=(1, 1), 16 | name=None): 17 | """Utility function to apply conv + BN. 18 | # Arguments 19 | x: input tensor. 20 | filters: filters in `Conv2D`. 21 | num_row: height of the convolution kernel. 22 | num_col: width of the convolution kernel. 23 | padding: padding mode in `Conv2D`. 24 | strides: strides in `Conv2D`. 25 | name: name of the ops; will become `name + '_conv'` 26 | for the convolution and `name + '_bn'` for the 27 | batch norm layer. 28 | # Returns 29 | Output tensor after applying `Conv2D` and `BatchNormalization`. 30 | """ 31 | if name is not None: 32 | bn_name = name + '_bn' 33 | conv_name = name + '_conv' 34 | else: 35 | bn_name = None 36 | conv_name = None 37 | if K.image_data_format() == 'channels_first': 38 | bn_axis = 1 39 | else: 40 | bn_axis = 3 41 | x = Conv2D( 42 | filters, (num_row, num_col), 43 | strides=strides, 44 | padding=padding, 45 | use_bias=False, 46 | name=conv_name)(x) 47 | x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x) 48 | x = Activation('relu', name=name)(x) 49 | return x 50 | 51 | #Light version of inceptionV3 - inceptionV3m (modified) 52 | def inceptionV3m(input_shape, classes): 53 | img_input = Input(shape=input_shape) 54 | 55 | if K.image_data_format() == 'channels_first': 56 | channel_axis = 1 57 | else: 58 | channel_axis = 3 59 | 60 | #x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding='valid') 61 | x = conv2d_bn(img_input, 32, 3, 3, padding='valid') 62 | x = conv2d_bn(x, 64, 3, 3) 63 | x = MaxPooling2D((3, 3), strides=(2, 2))(x) 64 | 65 | x = conv2d_bn(x, 80, 1, 1, padding='valid') 66 | x = conv2d_bn(x, 192, 3, 3, padding='valid') 67 | x = MaxPooling2D((3, 3), strides=(2, 2))(x) 68 | 69 | # mixed 0, 1, 2: 35 x 35 x 256 70 | branch1x1 = conv2d_bn(x, 64, 1, 1) 71 | 72 | branch5x5 = conv2d_bn(x, 48, 1, 1) 73 | branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) 74 | 75 | branch3x3dbl = conv2d_bn(x, 64, 1, 1) 76 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 77 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 78 | 79 | branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) 80 | branch_pool = conv2d_bn(branch_pool, 32, 1, 1) 81 | x = layers.concatenate( 82 | [branch1x1, branch5x5, branch3x3dbl, branch_pool], 83 | axis=channel_axis, 84 | name='mixed0') 85 | 86 | # mixed 1: 35 x 35 x 256 87 | branch1x1 = conv2d_bn(x, 64, 1, 1) 88 | 89 | branch5x5 = conv2d_bn(x, 48, 1, 1) 90 | branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) 91 | 92 | branch3x3dbl = conv2d_bn(x, 64, 1, 1) 93 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 94 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 95 | 96 | branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) 97 | branch_pool = conv2d_bn(branch_pool, 64, 1, 1) 98 | x = layers.concatenate( 99 | [branch1x1, branch5x5, branch3x3dbl, branch_pool], 100 | axis=channel_axis, 101 | name='mixed1') 102 | 103 | # mixed 2: 35 x 35 x 256 104 | branch1x1 = conv2d_bn(x, 64, 1, 1) 105 | 106 | branch5x5 = conv2d_bn(x, 48, 1, 1) 107 | branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) 108 | 109 | branch3x3dbl = conv2d_bn(x, 64, 1, 1) 110 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 111 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 112 | 113 | branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) 114 | branch_pool = conv2d_bn(branch_pool, 64, 1, 1) 115 | x = layers.concatenate( 116 | [branch1x1, branch5x5, branch3x3dbl, branch_pool], 117 | axis=channel_axis, 118 | name='mixed2') 119 | 120 | # mixed 3: 17 x 17 x 768 121 | branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid') 122 | 123 | branch3x3dbl = conv2d_bn(x, 64, 1, 1) 124 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 125 | branch3x3dbl = conv2d_bn( 126 | branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid') 127 | 128 | branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) 129 | x = layers.concatenate( 130 | [branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed3') 131 | 132 | # mixed 4: 17 x 17 x 768 133 | branch1x1 = conv2d_bn(x, 192, 1, 1) 134 | 135 | branch7x7 = conv2d_bn(x, 128, 1, 1) 136 | branch7x7 = conv2d_bn(branch7x7, 128, 1, 7) 137 | branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) 138 | 139 | branch7x7dbl = conv2d_bn(x, 128, 1, 1) 140 | branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) 141 | branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7) 142 | branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) 143 | branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) 144 | 145 | branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) 146 | branch_pool = conv2d_bn(branch_pool, 192, 1, 1) 147 | x = layers.concatenate( 148 | [branch1x1, branch7x7, branch7x7dbl, branch_pool], 149 | axis=channel_axis, 150 | name='mixed4') 151 | 152 | # Classification block 153 | x = GlobalAveragePooling2D(name='avg_pool')(x) 154 | #x = Dense(200, activation='relu', name='fc01')(x) 155 | #x = Dropout(0.5, name='dr01')(x) 156 | x = Dense(classes, activation='softmax', name='fc5')(x) 157 | 158 | inputs = img_input 159 | 160 | model = Model(inputs, x, name='inceptionV3m') 161 | 162 | return model 163 | 164 | #Another light version of inceptionV3 with only inception blocks in feature extractor - inceptionV3Only 165 | def inceptionV3Only(input_shape, classes): 166 | img_input = Input(shape=input_shape) 167 | if K.image_data_format() == 'channels_first': 168 | channel_axis = 1 169 | else: 170 | channel_axis = 3 171 | 172 | x = conv2d_bn(img_input, 64, 1, 1, padding='valid') 173 | 174 | # mixed 0, 1, 2: 35 x 35 x 256 175 | branch1x1 = conv2d_bn(x, 64, 1, 1) 176 | 177 | branch5x5 = conv2d_bn(x, 48, 1, 1) 178 | branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) 179 | 180 | branch3x3dbl = conv2d_bn(x, 64, 1, 1) 181 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 182 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 183 | 184 | branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) 185 | branch_pool = conv2d_bn(branch_pool, 32, 1, 1) 186 | x = layers.concatenate( 187 | [branch1x1, branch5x5, branch3x3dbl, branch_pool], 188 | axis=channel_axis, 189 | name='mixed0') 190 | 191 | # mixed 1: 35 x 35 x 256 192 | branch1x1 = conv2d_bn(x, 64, 1, 1) 193 | 194 | branch5x5 = conv2d_bn(x, 48, 1, 1) 195 | branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) 196 | 197 | branch3x3dbl = conv2d_bn(x, 64, 1, 1) 198 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 199 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 200 | 201 | branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) 202 | branch_pool = conv2d_bn(branch_pool, 64, 1, 1) 203 | x = layers.concatenate( 204 | [branch1x1, branch5x5, branch3x3dbl, branch_pool], 205 | axis=channel_axis, 206 | name='mixed1') 207 | 208 | # mixed 2: 35 x 35 x 256 209 | branch1x1 = conv2d_bn(x, 64, 1, 1) 210 | 211 | branch5x5 = conv2d_bn(x, 48, 1, 1) 212 | branch5x5 = conv2d_bn(branch5x5, 64, 5, 5) 213 | 214 | branch3x3dbl = conv2d_bn(x, 64, 1, 1) 215 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 216 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 217 | 218 | branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) 219 | branch_pool = conv2d_bn(branch_pool, 64, 1, 1) 220 | x = layers.concatenate( 221 | [branch1x1, branch5x5, branch3x3dbl, branch_pool], 222 | axis=channel_axis, 223 | name='mixed2') 224 | 225 | # mixed 3: 17 x 17 x 768 226 | branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding='valid') 227 | 228 | branch3x3dbl = conv2d_bn(x, 64, 1, 1) 229 | branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3) 230 | branch3x3dbl = conv2d_bn( 231 | branch3x3dbl, 96, 3, 3, strides=(2, 2), padding='valid') 232 | 233 | branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) 234 | x = layers.concatenate( 235 | [branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name='mixed3') 236 | 237 | # mixed 4: 17 x 17 x 768 238 | branch1x1 = conv2d_bn(x, 192, 1, 1) 239 | 240 | branch7x7 = conv2d_bn(x, 128, 1, 1) 241 | branch7x7 = conv2d_bn(branch7x7, 128, 1, 7) 242 | branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) 243 | 244 | branch7x7dbl = conv2d_bn(x, 128, 1, 1) 245 | branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) 246 | branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7) 247 | branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1) 248 | branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) 249 | 250 | branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) 251 | branch_pool = conv2d_bn(branch_pool, 192, 1, 1) 252 | x = layers.concatenate( 253 | [branch1x1, branch7x7, branch7x7dbl, branch_pool], 254 | axis=channel_axis, 255 | name='mixed4') 256 | 257 | # mixed 5, 6: 17 x 17 x 768 258 | """for i in range(1): 259 | branch1x1 = conv2d_bn(x, 192, 1, 1) 260 | 261 | branch7x7 = conv2d_bn(x, 160, 1, 1) 262 | branch7x7 = conv2d_bn(branch7x7, 160, 1, 7) 263 | branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) 264 | 265 | branch7x7dbl = conv2d_bn(x, 160, 1, 1) 266 | branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) 267 | branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7) 268 | branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1) 269 | branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) 270 | 271 | branch_pool = AveragePooling2D( 272 | (3, 3), strides=(1, 1), padding='same')(x) 273 | branch_pool = conv2d_bn(branch_pool, 192, 1, 1) 274 | x = layers.concatenate( 275 | [branch1x1, branch7x7, branch7x7dbl, branch_pool], 276 | axis=channel_axis, 277 | name='mixed' + str(5 + i)) 278 | 279 | # mixed 7: 17 x 17 x 768 280 | branch1x1 = conv2d_bn(x, 192, 1, 1) 281 | 282 | branch7x7 = conv2d_bn(x, 192, 1, 1) 283 | branch7x7 = conv2d_bn(branch7x7, 192, 1, 7) 284 | branch7x7 = conv2d_bn(branch7x7, 192, 7, 1) 285 | 286 | branch7x7dbl = conv2d_bn(x, 192, 1, 1) 287 | branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) 288 | branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) 289 | branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1) 290 | branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7) 291 | 292 | branch_pool = AveragePooling2D((3, 3), strides=(1, 1), padding='same')(x) 293 | branch_pool = conv2d_bn(branch_pool, 192, 1, 1) 294 | x = layers.concatenate( 295 | [branch1x1, branch7x7, branch7x7dbl, branch_pool], 296 | axis=channel_axis, 297 | name='mixed7') 298 | """ 299 | # mixed 8: 8 x 8 x 1280 300 | branch3x3 = conv2d_bn(x, 192, 1, 1) 301 | branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, 302 | strides=(2, 2), padding='valid') 303 | 304 | branch7x7x3 = conv2d_bn(x, 192, 1, 1) 305 | branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7) 306 | branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1) 307 | branch7x7x3 = conv2d_bn( 308 | branch7x7x3, 192, 3, 3, strides=(2, 2), padding='valid') 309 | 310 | branch_pool = MaxPooling2D((3, 3), strides=(2, 2))(x) 311 | x = layers.concatenate( 312 | [branch3x3, branch7x7x3, branch_pool], axis=channel_axis, name='mixed8') 313 | 314 | # mixed 9: 8 x 8 x 2048 315 | for i in range(1): 316 | branch1x1 = conv2d_bn(x, 320, 1, 1) 317 | 318 | branch3x3 = conv2d_bn(x, 384, 1, 1) 319 | branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3) 320 | branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1) 321 | branch3x3 = layers.concatenate( 322 | [branch3x3_1, branch3x3_2], axis=channel_axis, name='mixed9_' + str(i)) 323 | 324 | branch3x3dbl = conv2d_bn(x, 448, 1, 1) 325 | branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3) 326 | branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3) 327 | branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1) 328 | branch3x3dbl = layers.concatenate( 329 | [branch3x3dbl_1, branch3x3dbl_2], axis=channel_axis) 330 | 331 | branch_pool = AveragePooling2D( 332 | (3, 3), strides=(1, 1), padding='same')(x) 333 | branch_pool = conv2d_bn(branch_pool, 192, 1, 1) 334 | x = layers.concatenate( 335 | [branch1x1, branch3x3, branch3x3dbl, branch_pool], 336 | axis=channel_axis, 337 | name='mixed' + str(9 + i)) 338 | 339 | # Classification block 340 | x = GlobalAveragePooling2D(name='avg_pool')(x) 341 | # x = Dense(200, activation='relu', name='fc01')(x) 342 | # x = Dropout(0.5, name='dr01')(x) 343 | x = Dense(classes, activation='softmax', name='fc5')(x) 344 | 345 | inputs = img_input 346 | 347 | model = Model(inputs, x, name='InceptionV31Angle') 348 | return model -------------------------------------------------------------------------------- /modelResNetM.py: -------------------------------------------------------------------------------- 1 | 2 | from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img 3 | from keras.models import Sequential, Model 4 | from keras import layers 5 | from keras.layers import Conv2D, MaxPooling2D, BatchNormalization,Input, AveragePooling2D 6 | from keras.layers import Activation, Flatten, Dense 7 | from keras import backend as K 8 | 9 | # Reference: - [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) 10 | def identity_block(input_tensor, kernel_size, filters, stage, block): 11 | """The identity block is the block that has no conv layer at shortcut. 12 | # Arguments 13 | input_tensor: input tensor 14 | kernel_size: default 3, the kernel size of middle conv layer at main path 15 | filters: list of integers, the filters of 3 conv layer at main path 16 | stage: integer, current stage label, used for generating layer names 17 | block: 'a','b'..., current block label, used for generating layer names 18 | # Returns 19 | Output tensor for the block. 20 | """ 21 | filters1, filters2, filters3 = filters 22 | if K.image_data_format() == 'channels_last': 23 | bn_axis = 3 24 | else: 25 | bn_axis = 1 26 | conv_name_base = 'res' + str(stage) + block + '_branch' 27 | bn_name_base = 'bn' + str(stage) + block + '_branch' 28 | 29 | x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor) 30 | x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) 31 | x = Activation('relu')(x) 32 | 33 | x = Conv2D(filters2, kernel_size, 34 | padding='same', name=conv_name_base + '2b')(x) 35 | x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) 36 | x = Activation('relu')(x) 37 | 38 | x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x) 39 | x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) 40 | 41 | x = layers.add([x, input_tensor]) 42 | x = Activation('relu')(x) 43 | return x 44 | 45 | def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)): 46 | """A block that has a conv layer at shortcut. 47 | # Arguments 48 | input_tensor: input tensor 49 | kernel_size: default 3, the kernel size of middle conv layer at main path 50 | filters: list of integers, the filters of 3 conv layer at main path 51 | stage: integer, current stage label, used for generating layer names 52 | block: 'a','b'..., current block label, used for generating layer names 53 | # Returns 54 | Output tensor for the block. 55 | Note that from stage 3, the first conv layer at main path is with strides=(2,2) 56 | And the shortcut should have strides=(2,2) as well 57 | """ 58 | filters1, filters2, filters3 = filters 59 | if K.image_data_format() == 'channels_last': 60 | bn_axis = 3 61 | else: 62 | bn_axis = 1 63 | conv_name_base = 'res' + str(stage) + block + '_branch' 64 | bn_name_base = 'bn' + str(stage) + block + '_branch' 65 | 66 | x = Conv2D(filters1, (1, 1), strides=strides, 67 | name=conv_name_base + '2a')(input_tensor) 68 | x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) 69 | x = Activation('relu')(x) 70 | 71 | x = Conv2D(filters2, kernel_size, padding='same', 72 | name=conv_name_base + '2b')(x) 73 | x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) 74 | x = Activation('relu')(x) 75 | 76 | x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x) 77 | x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) 78 | 79 | shortcut = Conv2D(filters3, (1, 1), strides=strides, 80 | name=conv_name_base + '1')(input_tensor) 81 | shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut) 82 | 83 | x = layers.add([x, shortcut]) 84 | x = Activation('relu')(x) 85 | return x 86 | 87 | #Light version of ResNet for small images - resNetM (modified) 88 | def resNetM(input_shape, classes): 89 | img_input = Input(shape=input_shape) 90 | 91 | if K.image_data_format() == 'channels_last': 92 | bn_axis = 3 93 | else: 94 | bn_axis = 1 95 | 96 | x = Conv2D(64, (7, 7), strides=(2, 2), padding='same', name='conv1')(img_input) 97 | x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) 98 | x = Activation('relu')(x) 99 | x = MaxPooling2D((3, 3), strides=(2, 2))(x) 100 | 101 | x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) 102 | x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') 103 | x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') 104 | 105 | x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') 106 | x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') 107 | x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') 108 | x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') 109 | 110 | x = AveragePooling2D((5, 5), name='avg_pool')(x) 111 | 112 | x = Flatten()(x) 113 | #x = Dense(200, activation='relu', name='fc01')(x) 114 | #x = Dropout(0.5, name='dr01')(x) 115 | x = Dense(classes, activation='softmax', name='fc5')(x) 116 | 117 | inputs = img_input 118 | 119 | model = Model(inputs, x, name='resnetM') 120 | 121 | return model 122 | -------------------------------------------------------------------------------- /modelVGGm.py: -------------------------------------------------------------------------------- 1 | from keras.models import Sequential 2 | from keras.layers import Conv2D, MaxPooling2D 3 | from keras.layers import Activation, Dropout, Flatten, Dense 4 | 5 | #Light version of VGG for small images - Inspired from VGG, 2014 - VGGm(modified) 6 | def VGGm(input_shape, classes): 7 | 8 | model = Sequential() 9 | model.add(Conv2D(64, (7, 7), input_shape=input_shape)) 10 | model.add(Activation('relu')) 11 | model.add(Conv2D(64, (3, 3), input_shape=input_shape)) 12 | model.add(Activation('relu')) 13 | model.add(MaxPooling2D(pool_size=(2, 2))) 14 | 15 | model.add(Conv2D(128, (3, 3))) 16 | model.add(Activation('relu')) 17 | model.add(Conv2D(128, (3, 3))) 18 | model.add(Activation('relu')) 19 | model.add(MaxPooling2D(pool_size=(2, 2))) 20 | 21 | model.add(Conv2D(256, (3, 3))) 22 | model.add(Activation('relu')) 23 | model.add(Conv2D(256, (3, 3))) 24 | model.add(Activation('relu')) 25 | model.add(Conv2D(256, (3, 3))) 26 | model.add(Activation('relu')) 27 | 28 | model.add(Flatten()) 29 | model.add(Dense(200)) 30 | model.add(Activation('relu')) 31 | model.add(Dropout(0.5)) 32 | model.add(Dense(classes)) 33 | model.add(Activation('softmax')) 34 | 35 | return model 36 | -------------------------------------------------------------------------------- /testFunctions.py: -------------------------------------------------------------------------------- 1 | 2 | from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img 3 | from keras import backend as K 4 | import time 5 | import os 6 | import numpy as np 7 | from shutil import copyfile 8 | 9 | def test_image(imagepath, img_width, img_height, model): 10 | img = load_img(imagepath, target_size=(img_width, img_height)) 11 | x = img_to_array(img) # this is a Numpy array with shape (3, 150, 150) 12 | x = x / 255 13 | x = x.reshape((1,) + x.shape) # this is a Numpy array with shape (1, 3, 150, 150) 14 | start = time.time() 15 | out = model.predict(x, batch_size=1) 16 | stop = time.time() 17 | sec = stop - start 18 | val2 = out.max() 19 | maxind = out.argmax() 20 | print('Image {0} Class = {1} Predict = {2} PredictTime = {3}'.format(imagepath, maxind, out, sec)) 21 | 22 | def test_two_image(imagepath1, imagepath2, img_width, img_height, model): 23 | if K.image_data_format() == 'channels_first': 24 | input_shape = (3, img_width, img_height) 25 | else: 26 | input_shape = (img_width, img_height, 3) 27 | x = np.zeros((2,)+input_shape, dtype=K.floatx()) 28 | img = load_img(imagepath1, target_size=(img_width, img_height)) 29 | x_1 = img_to_array(img) # this is a Numpy array with shape (3, 150, 150) 30 | x_1 = x_1 / 255 31 | x[0] = x_1 32 | img = load_img(imagepath2, target_size=(img_width, img_height)) 33 | x_2 = img_to_array(img) # this is a Numpy array with shape (3, 150, 150) 34 | x_2 = x_2 / 255 35 | x[1] = x_2 36 | #x_1 = x.reshape((1,) + x.shape) # this is a Numpy array with shape (1, 3, 150, 150) 37 | start = time.time() 38 | out = model.predict(x, batch_size=1) 39 | stop = time.time() 40 | sec = stop - start 41 | #val2 = out.max() 42 | #maxind = out.argmax() 43 | print('Predict = {0} PredictTime = {1}'.format(out, sec)) 44 | 45 | def test_image_gen(imagepath, img_width, img_height, model): 46 | img = load_img(imagepath, target_size=(img_width, img_height)) 47 | x = img_to_array(img) # this is a Numpy array with shape (3, 150, 150) 48 | x = x.reshape((1,) + x.shape) # this is a Numpy array with shape (1, 3, 150, 150) 49 | sample_datagen = ImageDataGenerator(rescale=1. / 255) 50 | sample_gen = sample_datagen.flow(x, batch_size=1) 51 | 52 | start = time.time() 53 | out = model.predict_generator(sample_gen, 1) 54 | stop = time.time() 55 | sec = stop - start 56 | val2 = out.max() 57 | maxind = out.argmax() 58 | print('Image {0} Class = {1} MaxPredict = {2} PredictTime = {3}'.format(imagepath, maxind, val2, sec)) 59 | 60 | def clearFolder(target_path): 61 | # delete all files in the directory 62 | filesToRemove = [f for f in os.listdir(target_path)] 63 | for f in filesToRemove: 64 | os.remove(os.path.join(target_path, f)) 65 | 66 | def test_path_gen(path, img_width, img_height, model, save_e_path = '', save_tune_path = '',save_errors=False, clear_tune_path=True): 67 | little_datagen = ImageDataGenerator(rescale=1. / 255) 68 | test_dir = path 69 | little_generator = little_datagen.flow_from_directory( 70 | test_dir, 71 | target_size=(img_width, img_height), 72 | batch_size=1, 73 | class_mode=None, 74 | shuffle=False) 75 | start = time.time() 76 | out = model.predict_generator(little_generator, little_generator.n) 77 | #print(out) 78 | #print(little_generator.filenames) 79 | stop = time.time() 80 | sec = stop - start 81 | print("predict time = %.4f sec" % sec, end=' ') 82 | #print(out) 83 | 84 | #create result folder 85 | if(save_errors): 86 | path_list = list(little_generator.class_indices.keys()) 87 | path_list.sort() 88 | os.makedirs(save_e_path, exist_ok=True) 89 | os.makedirs(save_tune_path, exist_ok=True) 90 | for folder in path_list: 91 | target_path = os.path.join(save_e_path,folder) 92 | os.makedirs(target_path, exist_ok=True) 93 | clearFolder(target_path) 94 | target_path2 = os.path.join(save_tune_path, folder) 95 | os.makedirs(target_path2, exist_ok=True) 96 | if clear_tune_path: 97 | clearFolder(target_path2) 98 | 99 | #calc precision and recall for each class 100 | 101 | precision = [] 102 | recall = [] 103 | n_classes = little_generator.num_classes 104 | n_images = little_generator.n 105 | treshold = 0.5 106 | labels = little_generator.class_indices.items() 107 | print (labels) 108 | accuracy = 0 109 | all_facts = n_images 110 | real_facts = 0 111 | for i in range(n_classes): 112 | #calc precision and recall for i-th class. i starts from 0 113 | precision.append(0) 114 | recall.append(0) 115 | tp = 0 116 | fp = 0 117 | fn = 0 118 | for j in range(n_images): 119 | im_true_class = little_generator.classes[j] 120 | im_predict_result = out[j].max() 121 | im_predict_class = out[j].argmax() 122 | if (i == 0): 123 | #print('Image {0} Class = {1} MaxPredict = {2}'.format(j, im_predict_class, im_predict_result)) 124 | if(im_true_class == im_predict_class): 125 | real_facts += 1 126 | if(im_true_class != im_predict_class) and save_errors: 127 | save_filename = os.path.basename(little_generator.filenames[j]) 128 | copyfile(os.path.join(path,little_generator.filenames[j]), os.path.join(save_e_path, path_list[im_predict_class], save_filename)) 129 | copyfile(os.path.join(path, little_generator.filenames[j]), os.path.join(save_tune_path, path_list[im_true_class], 'tune_'+save_filename)) 130 | if (im_true_class == im_predict_class) and (im_predict_class == i): 131 | tp += 1 132 | else: 133 | if (im_predict_class == i) and (im_true_class != im_predict_class): 134 | fp += 1 135 | else: 136 | if (im_predict_class != i) and (im_true_class == i): 137 | fn += 1 138 | if((tp + fp) != 0): 139 | precision[i] = tp / (tp + fp) 140 | if ((tp + fn) != 0): 141 | recall[i] = tp / (tp + fn) 142 | accuracy = real_facts / all_facts 143 | print('Accuracy {0}'.format(accuracy)) 144 | print('Precision {0}'.format(precision)) 145 | print('Recall {0}'.format(recall)) 146 | --------------------------------------------------------------------------------