├── LICENSE ├── README.md ├── amazingkelly.jpeg ├── ckpt ├── checkpoint ├── emotion_model-30001.data-00000-of-00001 ├── emotion_model-30001.index ├── emotion_model-30001.meta └── place-models-here ├── data ├── emojis │ ├── angry.png │ ├── disgusted.png │ ├── fearful.png │ ├── happy.png │ ├── neutral.png │ ├── sad.png │ └── surprised.png └── haarcascade_files │ ├── haarcascade_eye.xml │ └── haarcascade_frontalface_default.xml ├── demo.py ├── main.py ├── model.py └── utils.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. 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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 | {project} Copyright (C) {year} {fullname} 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 | . -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![amazing](./amazingkelly.jpeg) 2 | 3 | Opensource deep learning framework [TensorFlow](https://www.tensorflow.org) is used in **Facial Expression Recognition(FER)**. 4 | The trained models achieved 65% accuracy in fer2013. If you like this, please give me a star. 5 | 6 | #### Dependencies 7 | 8 | FER requires: 9 | - Python (>= 3.3) 10 | - TensorFlow (>= 1.1.0) [Installation](https://www.tensorflow.org/install/) 11 | - OpenCV (python3-version) [Installation](http://docs.opencv.org/master/da/df6/tutorial_py_table_of_contents_setup.html) 12 | 13 | Only tested in Ubuntu and macOS Sierra. Other platforms are not sure work well. When problems meet, open an issue, I'll do my best to solve that. 14 | 15 | #### Usage 16 | ###### demo 17 | To run the demo, just type: 18 | ```shell 19 | python3 main.py 20 | ``` 21 | Then the program will creat a window to display the scene capture by webcamera. You need press SPACE key to capture face in current frame and recognize the facial expression. 22 | 23 | If you just want to run this demo instead of training the model from scaratch, the following content can be skipped. 24 | 25 | ###### train models 26 | If you want to train a model from scaratch by yourself, download the fer2013 datasets in [kaggle(91.97MB)](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data). Then extract the data to `data/fer2013` folder. 27 | 28 | It's is import that modifying the `MODE`(in `main.py`) from `demo` to `train` before you start training. 29 | Then type: 30 | ```shell 31 | python3 main.py 32 | ``` 33 | 34 | #### Issues & Suggestions 35 | If any issues and suggestions to me, you can create an [issue](https://github.com/xionghc/Facial-Expression-Recognition/issues/). 36 | -------------------------------------------------------------------------------- /amazingkelly.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/amazingkelly.jpeg -------------------------------------------------------------------------------- /ckpt/checkpoint: -------------------------------------------------------------------------------- 1 | model_checkpoint_path: "emotion_model-30001" 2 | all_model_checkpoint_paths: "emotion_model-30001" 3 | -------------------------------------------------------------------------------- /ckpt/emotion_model-30001.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/ckpt/emotion_model-30001.data-00000-of-00001 -------------------------------------------------------------------------------- /ckpt/emotion_model-30001.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/ckpt/emotion_model-30001.index -------------------------------------------------------------------------------- /ckpt/emotion_model-30001.meta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/ckpt/emotion_model-30001.meta -------------------------------------------------------------------------------- /ckpt/place-models-here: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/ckpt/place-models-here -------------------------------------------------------------------------------- /data/emojis/angry.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/data/emojis/angry.png -------------------------------------------------------------------------------- /data/emojis/disgusted.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/data/emojis/disgusted.png -------------------------------------------------------------------------------- /data/emojis/fearful.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/data/emojis/fearful.png -------------------------------------------------------------------------------- /data/emojis/happy.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/data/emojis/happy.png -------------------------------------------------------------------------------- /data/emojis/neutral.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/data/emojis/neutral.png -------------------------------------------------------------------------------- /data/emojis/sad.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/data/emojis/sad.png -------------------------------------------------------------------------------- /data/emojis/surprised.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xionghc/Facial-Expression-Recognition/13bd584f596d0a8794b32144163c8272afc94d48/data/emojis/surprised.png -------------------------------------------------------------------------------- /demo.py: -------------------------------------------------------------------------------- 1 | # /usr/bin/python3 2 | import cv2 3 | import numpy as np 4 | import sys 5 | import tensorflow as tf 6 | 7 | from model import predict, image_to_tensor, deepnn 8 | 9 | CASC_PATH = './data/haarcascade_files/haarcascade_frontalface_default.xml' 10 | cascade_classifier = cv2.CascadeClassifier(CASC_PATH) 11 | EMOTIONS = ['angry', 'disgusted', 'fearful', 'happy', 'sad', 'surprised', 'neutral'] 12 | 13 | def format_image(image): 14 | if len(image.shape) > 2 and image.shape[2] == 3: 15 | image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 16 | faces = cascade_classifier.detectMultiScale( 17 | image, 18 | scaleFactor = 1.3, 19 | minNeighbors = 5 20 | ) 21 | # None is no face found in image 22 | if not len(faces) > 0: 23 | return None, None 24 | max_are_face = faces[0] 25 | for face in faces: 26 | if face[2] * face[3] > max_are_face[2] * max_are_face[3]: 27 | max_are_face = face 28 | # face to image 29 | face_coor = max_are_face 30 | image = image[face_coor[1]:(face_coor[1] + face_coor[2]), face_coor[0]:(face_coor[0] + face_coor[3])] 31 | # Resize image to network size 32 | try: 33 | image = cv2.resize(image, (48, 48), interpolation=cv2.INTER_CUBIC) 34 | except Exception: 35 | print("[+} Problem during resize") 36 | return None, None 37 | return image, face_coor 38 | 39 | def face_dect(image): 40 | """ 41 | Detecting faces in image 42 | :param image: 43 | :return: the coordinate of max face 44 | """ 45 | if len(image.shape) > 2 and image.shape[2] == 3: 46 | image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 47 | faces = cascade_classifier.detectMultiScale( 48 | image, 49 | scaleFactor = 1.3, 50 | minNeighbors = 5 51 | ) 52 | if not len(faces) > 0: 53 | return None 54 | max_face = faces[0] 55 | for face in faces: 56 | if face[2] * face[3] > max_face[2] * max_face[3]: 57 | max_face = face 58 | face_image = image[max_face[1]:(max_face[1] + max_face[2]), max_face[0]:(max_face[0] + max_face[3])] 59 | try: 60 | image = cv2.resize(face_image, (48, 48), interpolation=cv2.INTER_CUBIC) / 255. 61 | except Exception: 62 | print("[+} Problem during resize") 63 | return None 64 | return face_image 65 | 66 | def resize_image(image, size): 67 | try: 68 | image = cv2.resize(image, size, interpolation=cv2.INTER_CUBIC) / 255. 69 | except Exception: 70 | print("+} Problem during resize") 71 | return None 72 | return image 73 | 74 | def draw_emotion(): 75 | pass 76 | 77 | def demo(modelPath, showBox=False): 78 | face_x = tf.placeholder(tf.float32, [None, 2304]) 79 | y_conv = deepnn(face_x) 80 | probs = tf.nn.softmax(y_conv) 81 | 82 | saver = tf.train.Saver() 83 | ckpt = tf.train.get_checkpoint_state(modelPath) 84 | sess = tf.Session() 85 | if ckpt and ckpt.model_checkpoint_path: 86 | saver.restore(sess, ckpt.model_checkpoint_path) 87 | print('Restore model sucsses!!\nNOTE: Press SPACE on keyboard to capture face.') 88 | 89 | feelings_faces = [] 90 | for index, emotion in enumerate(EMOTIONS): 91 | feelings_faces.append(cv2.imread('./data/emojis/' + emotion + '.png', -1)) 92 | video_captor = cv2.VideoCapture(0) 93 | 94 | emoji_face = [] 95 | result = None 96 | 97 | while True: 98 | ret, frame = video_captor.read() 99 | detected_face, face_coor = format_image(frame) 100 | if showBox: 101 | if face_coor is not None: 102 | [x,y,w,h] = face_coor 103 | cv2.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 2) 104 | 105 | if cv2.waitKey(1) & 0xFF == ord(' '): 106 | 107 | if detected_face is not None: 108 | cv2.imwrite('a.jpg', detected_face) 109 | tensor = image_to_tensor(detected_face) 110 | result = sess.run(probs, feed_dict={face_x: tensor}) 111 | # print(result) 112 | if result is not None: 113 | for index, emotion in enumerate(EMOTIONS): 114 | cv2.putText(frame, emotion, (10, index * 20 + 20), cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1) 115 | cv2.rectangle(frame, (130, index * 20 + 10), (130 + int(result[0][index] * 100), (index + 1) * 20 + 4), 116 | (255, 0, 0), -1) 117 | emoji_face = feelings_faces[np.argmax(result[0])] 118 | 119 | for c in range(0, 3): 120 | frame[200:320, 10:130, c] = emoji_face[:, :, c] * (emoji_face[:, :, 3] / 255.0) + frame[200:320, 10:130, c] * (1.0 - emoji_face[:, :, 3] / 255.0) 121 | cv2.imshow('face', frame) 122 | if cv2.waitKey(1) & 0xFF == ord('q'): 123 | break 124 | 125 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from demo import demo 2 | from model import train_model, valid_model 3 | import tensorflow as tf 4 | 5 | flags = tf.app.flags 6 | flags.DEFINE_string('MODE', 'demo', 7 | 'Set program to run in different mode, include train, valid and demo.') 8 | flags.DEFINE_string('checkpoint_dir', './ckpt', 9 | 'Path to model file.') 10 | flags.DEFINE_string('train_data', './data/fer2013/fer2013.csv', 11 | 'Path to training data.') 12 | flags.DEFINE_string('valid_data', './valid_sets/', 13 | 'Path to training data.') 14 | flags.DEFINE_boolean('show_box', False, 15 | 'If true, the results will show detection box') 16 | FLAGS = flags.FLAGS 17 | 18 | def main(): 19 | assert FLAGS.MODE in ('train', 'valid', 'demo') 20 | 21 | if FLAGS.MODE == 'demo': 22 | demo(FLAGS.checkpoint_dir, FLAGS.show_box) 23 | elif FLAGS.MODE == 'train': 24 | train_model(FLAGS.train_data) 25 | elif FLAGS.MODE == 'valid': 26 | valid_model(FLAGS.checkpoint_dir, FLAGS.valid_data) 27 | 28 | if __name__ == '__main__': 29 | main() 30 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | 4 | import numpy as np 5 | import tensorflow as tf 6 | 7 | from utils import * 8 | 9 | EMOTIONS = ['angry', 'disgusted', 'fearful', 'happy', 'sad', 'surprised', 'neutral'] 10 | 11 | def deepnn(x): 12 | x_image = tf.reshape(x, [-1, 48, 48, 1]) 13 | # conv1 14 | W_conv1 = weight_variables([5, 5, 1, 64]) 15 | b_conv1 = bias_variable([64]) 16 | h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 17 | # pool1 18 | h_pool1 = maxpool(h_conv1) 19 | # norm1 20 | norm1 = tf.nn.lrn(h_pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75) 21 | 22 | # conv2 23 | W_conv2 = weight_variables([3, 3, 64, 64]) 24 | b_conv2 = bias_variable([64]) 25 | h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 26 | norm2 = tf.nn.lrn(h_conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75) 27 | h_pool2 = maxpool(norm2) 28 | 29 | # Fully connected layer 30 | W_fc1 = weight_variables([12 * 12 * 64, 384]) 31 | b_fc1 = bias_variable([384]) 32 | h_conv3_flat = tf.reshape(h_pool2, [-1, 12 * 12 * 64]) 33 | h_fc1 = tf.nn.relu(tf.matmul(h_conv3_flat, W_fc1) + b_fc1) 34 | 35 | # Fully connected layer 36 | W_fc2 = weight_variables([384, 192]) 37 | b_fc2 = bias_variable([192]) 38 | h_fc2 = tf.matmul(h_fc1, W_fc2) + b_fc2 39 | 40 | # linear 41 | W_fc3 = weight_variables([192, 7]) 42 | b_fc3 = bias_variable([7]) 43 | y_conv = tf.add(tf.matmul(h_fc2, W_fc3), b_fc3) 44 | 45 | return y_conv 46 | 47 | 48 | def conv2d(x, W): 49 | return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 50 | 51 | 52 | def maxpool(x): 53 | return tf.nn.max_pool(x, ksize=[1, 3, 3, 1], 54 | strides=[1, 2, 2, 1], padding='SAME') 55 | 56 | 57 | def weight_variables(shape): 58 | initial = tf.truncated_normal(shape, stddev=0.1) 59 | return tf.Variable(initial) 60 | 61 | 62 | def bias_variable(shape): 63 | initial = tf.constant(0.1, shape=shape) 64 | return tf.Variable(initial) 65 | 66 | 67 | def train_model(train_data): 68 | fer2013 = input_data(train_data) 69 | max_train_steps = 30001 70 | 71 | x = tf.placeholder(tf.float32, [None, 2304]) 72 | y_ = tf.placeholder(tf.float32, [None, 7]) 73 | 74 | y_conv = deepnn(x) 75 | 76 | cross_entropy = tf.reduce_mean( 77 | tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) 78 | train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 79 | correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) 80 | accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 81 | 82 | with tf.Session() as sess: 83 | saver = tf.train.Saver() 84 | sess.run(tf.global_variables_initializer()) 85 | for step in range(max_train_steps): 86 | batch = fer2013.train.next_batch(50) 87 | if step % 100 == 0: 88 | train_accuracy = accuracy.eval(feed_dict={ 89 | x: batch[0], y_: batch[1]}) 90 | print('step %d, training accuracy %g' % (step, train_accuracy)) 91 | train_step.run(feed_dict={x: batch[0], y_: batch[1]}) 92 | 93 | if step + 1 == max_train_steps: 94 | saver.save(sess, './models/emotion_model', global_step=step + 1) 95 | if step % 1000 == 0: 96 | print('*Test accuracy %g' % accuracy.eval(feed_dict={ 97 | x: fer2013.validation.images, y_: fer2013.validation.labels})) 98 | 99 | 100 | def predict(image=[[0.1] * 2304]): 101 | x = tf.placeholder(tf.float32, [None, 2304]) 102 | y_conv = deepnn(x) 103 | 104 | # init = tf.global_variables_initializer() 105 | saver = tf.train.Saver() 106 | probs = tf.nn.softmax(y_conv) 107 | y_ = tf.argmax(probs) 108 | 109 | with tf.Session() as sess: 110 | # assert os.path.exists('/tmp/models/emotion_model') 111 | ckpt = tf.train.get_checkpoint_state('./models') 112 | print(ckpt.model_checkpoint_path) 113 | if ckpt and ckpt.model_checkpoint_path: 114 | saver.restore(sess, ckpt.model_checkpoint_path) 115 | print('Restore ssss') 116 | return sess.run(probs, feed_dict={x: image}) 117 | 118 | 119 | def image_to_tensor(image): 120 | tensor = np.asarray(image).reshape(-1, 2304) * 1 / 255.0 121 | return tensor 122 | 123 | 124 | def valid_model(modelPath, validFile): 125 | x = tf.placeholder(tf.float32, [None, 2304]) 126 | y_conv = deepnn(x) 127 | probs = tf.nn.softmax(y_conv) 128 | 129 | saver = tf.train.Saver() 130 | ckpt = tf.train.get_checkpoint_state(modelPath) 131 | 132 | with tf.Session() as sess: 133 | print(ckpt.model_checkpoint_path) 134 | if ckpt and ckpt.model_checkpoint_path: 135 | saver.restore(sess, ckpt.model_checkpoint_path) 136 | print('Restore model sucsses!!') 137 | 138 | files = os.listdir(validFile) 139 | 140 | for file in files: 141 | if file.endswith('.jpg'): 142 | image_file = os.path.join(validFile, file) 143 | image = cv2.imread(image_file, cv2.IMREAD_GRAYSCALE) 144 | tensor = image_to_tensor(image) 145 | result = sess.run(probs, feed_dict={x: tensor}) 146 | print(file, EMOTIONS[result.argmax()]) 147 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import collections 2 | 3 | import cv2 4 | import numpy as np 5 | import pandas as pd 6 | from tensorflow.python.framework import dtypes, random_seed 7 | 8 | 9 | def load_data(data_file): 10 | data = pd.read_csv(data_file) 11 | pixels = data['pixels'].tolist() 12 | width, height = 48, 48 13 | faces = [] 14 | i = 0 15 | for pixel_sequence in pixels: 16 | face = [int(pixel) for pixel in pixel_sequence.split(' ')] 17 | face = np.asarray(face).reshape(width, height) 18 | # if i< 100: 19 | # cv2.imwrite('./valid_sets/%03d.jpg'%(i), face) 20 | # print(i) 21 | # i += 1 22 | faces.append(face) 23 | # if i == 100: 24 | # return 25 | faces = np.asarray(faces) 26 | faces = np.expand_dims(faces, -1) 27 | emotions = pd.get_dummies(data['emotion']).as_matrix() 28 | return faces, emotions 29 | 30 | 31 | class DataSet(object): 32 | def __init__(self, 33 | images, 34 | labels, 35 | reshape=True, 36 | dtype=dtypes.float32, 37 | seed=None): 38 | seed1, seed2 = random_seed.get_seed(seed) 39 | np.random.seed(seed1 if seed is None else seed2) 40 | if reshape: 41 | assert images.shape[3] == 1 42 | images = images.reshape(images.shape[0], 43 | images.shape[1]*images.shape[2]) 44 | 45 | if dtype == dtypes.float32: 46 | images = images.astype(np.float32) 47 | images = np.multiply(images, 1.0 / 255.0) 48 | 49 | self._num_examples = images.shape[0] 50 | self._images = images 51 | self._labels = labels 52 | self._epochs_completed = 0 53 | self._index_in_epoch = 0 54 | 55 | @property 56 | def images(self): 57 | return self._images 58 | 59 | @property 60 | def labels(self): 61 | return self._labels 62 | 63 | @property 64 | def num_examples(self): 65 | return self.num_examples 66 | 67 | @property 68 | def epochs_completed(self): 69 | self._epochs_completed 70 | 71 | def next_batch(self, batch_size, shuffle=True): 72 | start = self._index_in_epoch 73 | # shuffle for the first epoch 74 | if self._epochs_completed == 0 and start == 0 and shuffle: 75 | perm0 = np.arange(self._num_examples) 76 | np.random.shuffle(perm0) 77 | self._images = self._images[perm0] 78 | self._labels = self._labels[perm0] 79 | # Go to the next epoch 80 | if start + batch_size > self._num_examples: 81 | self._epochs_completed += 1 82 | rest_num_examples = self._num_examples - start 83 | images_rest_part = self._images[start:self._num_examples] 84 | labels_rest_part = self._labels[start:self._num_examples] 85 | # Shuffle 86 | if shuffle: 87 | perm = np.arange(self._num_examples) 88 | np.random.shuffle(perm) 89 | self._images = self._images[perm] 90 | self._labels = self._labels[perm] 91 | start = 0 92 | self._index_in_epoch = batch_size - rest_num_examples 93 | end = self._index_in_epoch 94 | images_new_part = self._images[start:end] 95 | labels_new_part = self._labels[start:end] 96 | return np.concatenate((images_rest_part, images_new_part), axis=0), np.concatenate((labels_rest_part, labels_new_part), axis=0) 97 | else: 98 | self._index_in_epoch += batch_size 99 | end = self._index_in_epoch 100 | return self._images[start:end], self._labels[start:end] 101 | 102 | 103 | def input_data( 104 | train_dir, 105 | dtype=dtypes.float32, 106 | reshape=True, 107 | seed=None): 108 | training_size = 28709 109 | validation_size = 3589 110 | test_size = 3589 111 | 112 | train_faces, train_emotions = load_data(train_dir) 113 | print('Dataset load success!!') 114 | # Validation data 115 | validation_faces = train_faces[training_size : training_size + validation_size] 116 | validation_emotions = train_emotions[training_size : training_size + validation_size] 117 | # Test data 118 | test_faces = train_faces[training_size + validation_size : ] 119 | test_emotions = train_emotions[training_size + validation_size : ] 120 | # Training data 121 | train_faces = train_faces[ : training_size] 122 | train_emotions = train_emotions[ : training_size] 123 | 124 | Datasets = collections.namedtuple('Datasets', ['train', 'validation', 'test']) 125 | train = DataSet(train_faces, train_emotions, reshape=reshape, seed=seed) 126 | validation = DataSet(validation_faces, validation_emotions, dtype=dtype, reshape=reshape, seed=seed) 127 | test = DataSet(test_faces, test_emotions, dtype=dtype, reshape=reshape, seed=seed) 128 | return Datasets(train=train, validation=validation, test=test) 129 | 130 | def _test(): 131 | import cv2 132 | i = input_data('./data/fer2013/fer2013.csv') 133 | 134 | if __name__ == '__main__': 135 | _test() 136 | --------------------------------------------------------------------------------