├── .gitattributes ├── men.jpg ├── ng.jpg ├── image.jpg ├── ng1.jpeg ├── snap.jpg ├── output_16_1.png ├── output_16_2.png ├── output_17_1.png ├── output_18_0.png ├── output_19_2.png ├── .gitignore ├── Readme.md └── LICENSE /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /men.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ashishpatel26/Facial-Expression-Recognization-using-JAFFE/HEAD/men.jpg -------------------------------------------------------------------------------- /ng.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ashishpatel26/Facial-Expression-Recognization-using-JAFFE/HEAD/ng.jpg -------------------------------------------------------------------------------- /image.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ashishpatel26/Facial-Expression-Recognization-using-JAFFE/HEAD/image.jpg -------------------------------------------------------------------------------- /ng1.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ashishpatel26/Facial-Expression-Recognization-using-JAFFE/HEAD/ng1.jpeg -------------------------------------------------------------------------------- /snap.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ashishpatel26/Facial-Expression-Recognization-using-JAFFE/HEAD/snap.jpg -------------------------------------------------------------------------------- /output_16_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ashishpatel26/Facial-Expression-Recognization-using-JAFFE/HEAD/output_16_1.png -------------------------------------------------------------------------------- /output_16_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ashishpatel26/Facial-Expression-Recognization-using-JAFFE/HEAD/output_16_2.png -------------------------------------------------------------------------------- /output_17_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ashishpatel26/Facial-Expression-Recognization-using-JAFFE/HEAD/output_17_1.png -------------------------------------------------------------------------------- /output_18_0.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ashishpatel26/Facial-Expression-Recognization-using-JAFFE/HEAD/output_18_0.png -------------------------------------------------------------------------------- /output_19_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ashishpatel26/Facial-Expression-Recognization-using-JAFFE/HEAD/output_19_2.png -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *.cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # Jupyter Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # SageMath parsed files 79 | *.sage.py 80 | 81 | # Environments 82 | .env 83 | .venv 84 | env/ 85 | venv/ 86 | ENV/ 87 | 88 | # Spyder project settings 89 | .spyderproject 90 | .spyproject 91 | 92 | # Rope project settings 93 | .ropeproject 94 | 95 | # mkdocs documentation 96 | /site 97 | 98 | # mypy 99 | .mypy_cache/ 100 | -------------------------------------------------------------------------------- /Readme.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ![snap](snap.jpg) 4 | 5 | **Dataset : https://zenodo.org/record/3451524#.X2bg0GgzZPY** 6 | 7 | #### Notebook 8 | 9 | --- 10 | 11 | # 1. Import All Required Packages 12 | 13 | 14 | ```python 15 | # Import libraries# Impor 16 | import os,cv2 17 | import numpy as np 18 | import matplotlib.pyplot as plt 19 | import matplotlib.image as mpimg 20 | from pylab import rcParams 21 | rcParams['figure.figsize'] = 20, 10 22 | 23 | from sklearn.utils import shuffle 24 | from sklearn.cross_validation import train_test_split 25 | 26 | import keras 27 | 28 | from keras.utils import np_utils 29 | 30 | from keras import backend as K 31 | 32 | from keras.models import Sequential 33 | from keras.layers.core import Dense, Dropout, Activation, Flatten 34 | from keras.layers.convolutional import Convolution2D, MaxPooling2D 35 | from keras.optimizers import SGD,RMSprop,adam 36 | from keras.preprocessing.image import ImageDataGenerator 37 | ``` 38 | 39 | # 2.Read Images from the data path 40 | 41 | 42 | ```python 43 | #Define Datapath 44 | data_path = './jaffe/' 45 | data_dir_list = os.listdir(data_path) 46 | 47 | img_rows=256 48 | img_cols=256 49 | num_channel=1 50 | 51 | num_epoch=10 52 | 53 | img_data_list=[] 54 | 55 | 56 | for dataset in data_dir_list: 57 | img_list=os.listdir(data_path+'/'+ dataset) 58 | print ('Loaded the images of dataset-'+'{}\n'.format(dataset)) 59 | for img in img_list: 60 | input_img=cv2.imread(data_path + '/'+ dataset + '/'+ img ) 61 | #input_img=cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY) 62 | input_img_resize=cv2.resize(input_img,(128,128)) 63 | img_data_list.append(input_img_resize) 64 | 65 | img_data = np.array(img_data_list) 66 | img_data = img_data.astype('float32') 67 | img_data = img_data/255 68 | img_data.shape 69 | ``` 70 | 71 | Loaded the images of dataset-.ipynb_checkpoints 72 | 73 | Loaded the images of dataset-ANGRY 74 | 75 | Loaded the images of dataset-DISGUST 76 | 77 | Loaded the images of dataset-FEAR 78 | 79 | Loaded the images of dataset-HAPPY 80 | 81 | Loaded the images of dataset-NEUTRAL 82 | 83 | Loaded the images of dataset-SAD 84 | 85 | Loaded the images of dataset-SURPRISE 86 | 87 | 88 | ​ 89 | 90 | 91 | 92 | 93 | (213, 128, 128, 3) 94 | 95 | 96 | 97 | # 3.Define the number of classes 98 | 99 | 100 | ```python 101 | num_classes = 7 102 | 103 | num_of_samples = img_data.shape[0] 104 | labels = np.ones((num_of_samples,),dtype='int64') 105 | 106 | labels[0:29]=0 #30 107 | labels[30:59]=1 #29 108 | labels[60:92]=2 #32 109 | labels[93:124]=3 #31 110 | labels[125:155]=4 #30 111 | labels[156:187]=5 #31 112 | labels[188:]=6 #30 113 | 114 | names = ['ANGRY','DISGUST','FEAR','HAPPY','NEUTRAL','SAD','SURPRISE'] 115 | 116 | def getLabel(id): 117 | return ['ANGRY','DISGUST','FEAR','HAPPY','NEUTRAL','SAD','SURPRISE'][id] 118 | ``` 119 | 120 | # 4.Convert the class label to one-hot encoding 121 | 122 | 123 | ```python 124 | # convert class labels to on-hot encoding# conve 125 | Y = np_utils.to_categorical(labels, num_classes) 126 | 127 | #Shuffle the dataset 128 | x,y = shuffle(img_data,Y, random_state=2) 129 | # Split the dataset 130 | X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=2) 131 | ``` 132 | 133 | # 5. CNN Layer Set 134 | 135 | 136 | ```python 137 | # Defining the model 138 | 139 | input_shape=img_data[0].shape 140 | 141 | model = Sequential() 142 | 143 | # Feature Extraction 144 | # model.add(Convolution2D(32,3,3, border_mode='same',input_shape=input_shape)) 145 | # model.add(Activation('relu')) 146 | # model.add(Convolution2D(32, 3, 3)) 147 | # model.add(Activation('relu')) 148 | # model.add(MaxPooling2D(pool_size=(2, 2))) 149 | # model.add(Dropout(0.5)) 150 | 151 | # model.add(Convolution2D(64, 3, 3)) 152 | # model.add(Activation('relu')) 153 | # # model.add(Convolution2D(64, 3, 3)) 154 | # # model.add(Activation('relu')) 155 | # model.add(MaxPooling2D(pool_size=(2, 2))) 156 | # model.add(Dropout(0.5)) 157 | 158 | # model.add(Convolution2D(128, 3, 3)) 159 | # model.add(Activation('relu')) 160 | # # model.add(Convolution2D(128, 3, 3)) 161 | # # model.add(Activation('relu')) 162 | # model.add(MaxPooling2D(pool_size=(2, 2))) 163 | # model.add(Dropout(0.5)) 164 | 165 | #new Code 166 | model.add(Convolution2D(6, 5, 5, input_shape=input_shape, border_mode='same')) 167 | model.add(Activation('relu')) 168 | model.add(MaxPooling2D(pool_size=(2, 2))) 169 | 170 | model.add(Convolution2D(16, 5, 5, border_mode='same')) 171 | model.add(Activation('relu')) 172 | model.add(MaxPooling2D(pool_size=(2, 2))) 173 | 174 | model.add(Convolution2D(120, 5, 5)) 175 | model.add(Activation('relu')) 176 | model.add(Dropout(0.25)) 177 | 178 | model.add(Flatten()) 179 | model.add(Dense(84)) 180 | model.add(Activation('relu')) 181 | model.add(Dropout(0.5)) 182 | model.add(Dense(num_classes)) 183 | model.add(Activation('softmax')) 184 | 185 | # Classification 186 | # model.add(Flatten()) 187 | # model.add(Dense(64)) 188 | # model.add(Activation('relu')) 189 | # model.add(Dropout(0.5)) 190 | # model.add(Dense(num_classes)) 191 | # model.add(Activation('softmax')) 192 | 193 | #Compile Model 194 | model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=["accuracy"]) 195 | ``` 196 | 197 | C:\Users\ashish.patel\Anaconda3\lib\site-packages\ipykernel_launcher.py:30: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(6, (5, 5), input_shape=(128, 128,..., padding="same")` 198 | C:\Users\ashish.patel\Anaconda3\lib\site-packages\ipykernel_launcher.py:34: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(16, (5, 5), padding="same")` 199 | C:\Users\ashish.patel\Anaconda3\lib\site-packages\ipykernel_launcher.py:38: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(120, (5, 5))` 200 | 201 | 202 | # 6.View Model Configuration 203 | 204 | 205 | ```python 206 | model.summary() 207 | model.get_config() 208 | model.layers[0].get_config() 209 | model.layers[0].input_shape 210 | model.layers[0].output_shape 211 | model.layers[0].get_weights() 212 | np.shape(model.layers[0].get_weights()[0]) 213 | model.layers[0].trainable 214 | ``` 215 | 216 | _________________________________________________________________ 217 | Layer (type) Output Shape Param # 218 | ================================================================= 219 | conv2d_13 (Conv2D) (None, 128, 128, 6) 456 220 | _________________________________________________________________ 221 | activation_21 (Activation) (None, 128, 128, 6) 0 222 | _________________________________________________________________ 223 | max_pooling2d_9 (MaxPooling2 (None, 64, 64, 6) 0 224 | _________________________________________________________________ 225 | conv2d_14 (Conv2D) (None, 64, 64, 16) 2416 226 | _________________________________________________________________ 227 | activation_22 (Activation) (None, 64, 64, 16) 0 228 | _________________________________________________________________ 229 | max_pooling2d_10 (MaxPooling (None, 32, 32, 16) 0 230 | _________________________________________________________________ 231 | conv2d_15 (Conv2D) (None, 28, 28, 120) 48120 232 | _________________________________________________________________ 233 | activation_23 (Activation) (None, 28, 28, 120) 0 234 | _________________________________________________________________ 235 | dropout_10 (Dropout) (None, 28, 28, 120) 0 236 | _________________________________________________________________ 237 | flatten_5 (Flatten) (None, 94080) 0 238 | _________________________________________________________________ 239 | dense_9 (Dense) (None, 84) 7902804 240 | _________________________________________________________________ 241 | activation_24 (Activation) (None, 84) 0 242 | _________________________________________________________________ 243 | dropout_11 (Dropout) (None, 84) 0 244 | _________________________________________________________________ 245 | dense_10 (Dense) (None, 7) 595 246 | _________________________________________________________________ 247 | activation_25 (Activation) (None, 7) 0 248 | ================================================================= 249 | Total params: 7,954,391 250 | Trainable params: 7,954,391 251 | Non-trainable params: 0 252 | _________________________________________________________________ 253 | 254 | 255 | 256 | 257 | 258 | True 259 | 260 | 261 | 262 | 263 | ```python 264 | from keras import callbacks 265 | filename='model_train_new.csv' 266 | filepath="Best-weights-my_model-{epoch:03d}-{loss:.4f}-{acc:.4f}.hdf5" 267 | 268 | csv_log=callbacks.CSVLogger(filename, separator=',', append=False) 269 | checkpoint = callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min') 270 | callbacks_list = [csv_log,checkpoint] 271 | callbacks_list = [csv_log] 272 | ``` 273 | 274 | # 7. Train Model 275 | 276 | 277 | ```python 278 | # Training 279 | hist = model.fit(X_train, y_train, batch_size=7, nb_epoch=30, verbose=1, validation_data=(X_test, y_test),callbacks=callbacks_list) 280 | ``` 281 | 282 | C:\Users\ashish.patel\Anaconda3\lib\site-packages\keras\models.py:942: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`. 283 | warnings.warn('The `nb_epoch` argument in `fit` ' 284 | 285 | 286 | Train on 181 samples, validate on 32 samples 287 | Epoch 1/30 288 | 181/181 [==============================] - 6s 35ms/step - loss: 0.2098 - acc: 0.9171 - val_loss: 0.4530 - val_acc: 0.9063 289 | Epoch 2/30 290 | 181/181 [==============================] - 7s 36ms/step - loss: 0.2269 - acc: 0.9282 - val_loss: 0.3455 - val_acc: 0.8750 291 | Epoch 3/30 292 | 181/181 [==============================] - 6s 35ms/step - loss: 0.2118 - acc: 0.9006 - val_loss: 0.4345 - val_acc: 0.9063 293 | Epoch 4/30 294 | 181/181 [==============================] - 6s 35ms/step - loss: 0.1585 - acc: 0.9558 - val_loss: 0.4712 - val_acc: 0.8438 295 | Epoch 5/30 296 | 181/181 [==============================] - 6s 35ms/step - loss: 0.1346 - acc: 0.9392 - val_loss: 0.4494 - val_acc: 0.9063 297 | Epoch 6/30 298 | 181/181 [==============================] - 6s 34ms/step - loss: 0.1372 - acc: 0.9282 - val_loss: 0.5018 - val_acc: 0.8750 299 | Epoch 7/30 300 | 181/181 [==============================] - 6s 35ms/step - loss: 0.1456 - acc: 0.9392 - val_loss: 0.4257 - val_acc: 0.8750 301 | Epoch 8/30 302 | 181/181 [==============================] - 6s 35ms/step - loss: 0.1435 - acc: 0.9282 - val_loss: 0.3619 - val_acc: 0.9375 303 | Epoch 9/30 304 | 181/181 [==============================] - 6s 35ms/step - loss: 0.1538 - acc: 0.9392 - val_loss: 0.3632 - val_acc: 0.9063 305 | Epoch 10/30 306 | 181/181 [==============================] - 6s 35ms/step - loss: 0.0964 - acc: 0.9779 - val_loss: 0.4315 - val_acc: 0.9375 307 | Epoch 11/30 308 | 181/181 [==============================] - 6s 34ms/step - loss: 0.1538 - acc: 0.9337 - val_loss: 0.5474 - val_acc: 0.8438 309 | Epoch 12/30 310 | 181/181 [==============================] - 6s 35ms/step - loss: 0.1100 - acc: 0.9448 - val_loss: 0.5291 - val_acc: 0.9375 311 | Epoch 13/30 312 | 181/181 [==============================] - 6s 34ms/step - loss: 0.1660 - acc: 0.9282 - val_loss: 0.3775 - val_acc: 0.9063 313 | Epoch 14/30 314 | 181/181 [==============================] - 6s 35ms/step - loss: 0.0673 - acc: 0.9779 - val_loss: 0.5139 - val_acc: 0.8750 315 | Epoch 15/30 316 | 181/181 [==============================] - 6s 35ms/step - loss: 0.0870 - acc: 0.9724 - val_loss: 0.6909 - val_acc: 0.8750 317 | Epoch 16/30 318 | 181/181 [==============================] - 6s 35ms/step - loss: 0.1067 - acc: 0.9613 - val_loss: 0.6124 - val_acc: 0.8750 319 | Epoch 17/30 320 | 181/181 [==============================] - 6s 35ms/step - loss: 0.1258 - acc: 0.9558 - val_loss: 0.4615 - val_acc: 0.8438 321 | Epoch 18/30 322 | 181/181 [==============================] - 6s 34ms/step - loss: 0.1450 - acc: 0.9613 - val_loss: 0.4444 - val_acc: 0.8750 323 | Epoch 19/30 324 | 181/181 [==============================] - 6s 35ms/step - loss: 0.1225 - acc: 0.9558 - val_loss: 0.4193 - val_acc: 0.9063 325 | Epoch 20/30 326 | 181/181 [==============================] - 6s 35ms/step - loss: 0.0756 - acc: 0.9834 - val_loss: 0.4697 - val_acc: 0.9375 327 | Epoch 21/30 328 | 181/181 [==============================] - 6s 34ms/step - loss: 0.1110 - acc: 0.9503 - val_loss: 0.5044 - val_acc: 0.8750 329 | Epoch 22/30 330 | 181/181 [==============================] - 6s 35ms/step - loss: 0.1175 - acc: 0.9558 - val_loss: 0.5838 - val_acc: 0.8750 331 | Epoch 23/30 332 | 181/181 [==============================] - 6s 34ms/step - loss: 0.0677 - acc: 0.9669 - val_loss: 0.7173 - val_acc: 0.9063 333 | Epoch 24/30 334 | 181/181 [==============================] - 6s 34ms/step - loss: 0.0889 - acc: 0.9558 - val_loss: 0.6095 - val_acc: 0.8750 335 | Epoch 25/30 336 | 181/181 [==============================] - 6s 35ms/step - loss: 0.1079 - acc: 0.9503 - val_loss: 0.5208 - val_acc: 0.8438 337 | Epoch 26/30 338 | 181/181 [==============================] - 6s 34ms/step - loss: 0.1025 - acc: 0.9503 - val_loss: 0.4764 - val_acc: 0.9063 339 | Epoch 27/30 340 | 181/181 [==============================] - 6s 36ms/step - loss: 0.0592 - acc: 0.9779 - val_loss: 0.4826 - val_acc: 0.9063 341 | Epoch 28/30 342 | 181/181 [==============================] - 6s 34ms/step - loss: 0.1115 - acc: 0.9448 - val_loss: 0.3785 - val_acc: 0.9375 343 | Epoch 29/30 344 | 181/181 [==============================] - 6s 34ms/step - loss: 0.1058 - acc: 0.9337 - val_loss: 0.5347 - val_acc: 0.9063 345 | Epoch 30/30 346 | 181/181 [==============================] - 6s 36ms/step - loss: 0.1807 - acc: 0.9227 - val_loss: 0.4655 - val_acc: 0.9063 347 | 348 | 349 | 350 | ```python 351 | #Model Save 352 | model.save_weights('model_weights.h5') 353 | model.save('model_keras.h5') 354 | ``` 355 | 356 | 357 | ```python 358 | # visualizing losses and accuracy 359 | %matplotlib inline 360 | 361 | train_loss=hist.history['loss'] 362 | val_loss=hist.history['val_loss'] 363 | train_acc=hist.history['acc'] 364 | val_acc=hist.history['val_acc'] 365 | 366 | epochs = range(len(train_acc)) 367 | 368 | plt.plot(epochs,train_loss,'r', label='train_loss') 369 | plt.plot(epochs,val_loss,'b', label='val_loss') 370 | plt.title('train_loss vs val_loss') 371 | plt.legend() 372 | plt.figure() 373 | 374 | plt.plot(epochs,train_acc,'r', label='train_acc') 375 | plt.plot(epochs,val_acc,'b', label='val_acc') 376 | plt.title('train_acc vs val_acc') 377 | plt.legend() 378 | plt.figure() 379 | ``` 380 | 381 | 382 | 383 | 384 | 385 | 386 | 387 | 388 | 389 | ![png](output_16_1.png) 390 | 391 | 392 | 393 | ![png](output_16_2.png) 394 | 395 | 396 | 397 | 398 | 399 | 400 | 401 | ```python 402 | # Evaluating the model 403 | score = model.evaluate(X_test, y_test, verbose=0) 404 | print('Test Loss:', score[0]) 405 | print('Test accuracy:', score[1]) 406 | 407 | test_image = X_test[0:1] 408 | print (test_image.shape) 409 | 410 | print(model.predict(test_image)) 411 | print(model.predict_classes(test_image)) 412 | print(y_test[0:1]) 413 | 414 | res = model.predict_classes(X_test[:9]) 415 | plt.figure(figsize=(10, 10)) 416 | 417 | for i in range(0, 9): 418 | plt.subplot(330 + 1 + i) 419 | plt.imshow(X_test[i],cmap=plt.get_cmap('gray')) 420 | plt.gca().get_xaxis().set_ticks([]) 421 | plt.gca().get_yaxis().set_ticks([]) 422 | plt.ylabel('prediction = %s' % getLabel(res[i]), fontsize=14) 423 | # show the plot 424 | plt.show() 425 | 426 | ``` 427 | 428 | Test Loss: 0.4654931426048279 429 | Test accuracy: 0.90625 430 | (1, 128, 128, 3) 431 | [[2.1821804e-09 8.9050531e-08 3.2704984e-06 1.3671537e-04 9.9984014e-01 432 | 1.5250735e-05 4.4816770e-06]] 433 | [4] 434 | [[0. 0. 0. 0. 1. 0. 0.]] 435 | 436 | 437 | 438 | ![png](output_17_1.png) 439 | 440 | 441 | 442 | ```python 443 | from sklearn.metrics import confusion_matrix 444 | results = model.predict_classes(X_test) 445 | cm = confusion_matrix(np.where(y_test == 1)[1], results) 446 | plt.matshow(cm) 447 | plt.title('Confusion Matrix') 448 | plt.colorbar() 449 | plt.ylabel('True Label') 450 | plt.xlabel('Predicted Label') 451 | plt.show() 452 | ``` 453 | 454 | 455 | ![png](output_18_0.png) 456 | 457 | 458 | 459 | ```python 460 | testimg_data_list=[] 461 | test_img=cv2.imread('men.jpg', True) 462 | 463 | test_img_resize=cv2.resize(test_img,(128,128)) 464 | testimg_data_list.append(test_img_resize) 465 | testimg_data = np.array(testimg_data_list) 466 | testimg_data = testimg_data.astype('float32') 467 | testimg_data = testimg_data/255 468 | testimg_data.shape 469 | 470 | print("test image original shaape",testimg_data[0].shape) 471 | print("image original shaape",img_data[0].shape) 472 | 473 | results = model.predict_classes(testimg_data) 474 | plt.imshow(test_img,cmap=plt.get_cmap('Set2')) 475 | plt.gca().get_xaxis().set_ticks([]) 476 | plt.gca().get_yaxis().set_ticks([]) 477 | plt.xlabel('prediction = %s' % getLabel(results[0]), fontsize=25) 478 | 479 | ``` 480 | 481 | test image original shaape (128, 128, 3) 482 | image original shaape (128, 128, 3) 483 | 484 | 485 | 486 | 487 | 488 | Text(0.5,0,'prediction = SAD') 489 | 490 | 491 | 492 | 493 | ![png](output_19_2.png) 494 | 495 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. 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You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) 2018 {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Facial-Expression-Recognization-using-JAFFE Copyright (C) 2018 Ashish Patel 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . --------------------------------------------------------------------------------