Introduction

About

Team : The Elite

  • Vidhya Subramaniam
  • Devleena Banerjee
  • Priyank jha
  • Karthik chiranjeevi

Objective

Building A Mood Classifier Based On Facial Expressions - With AI and Computer Vision

The objective of this study is to classify mood of the person from facial expressions Images are categorized in three classes namely sadness, fear and happiness based on the emotion shown in the facial expressions .

Dataset

The data consists of 48x48 pixel grayscale images of faces. The pixel values are stored in 2304 (48*48) columns. These column names start with pixel. Along with pixel values, there is emotion column that say about mood of the image.

The task is to categorize each face based on the emotion shown in the facial expression in to one of three categories.

Along with pixel values, aithon2020_level2_traning.csv dataset contains another column emotion that say about mood that is present in the image. This is the dataset you will use to train your model.

The final test set, which will be used to determine the winner of the competition, will be published later.

https://spotleai.sgp1.digitaloceanspaces.com/course/zip/aithon2020-level-2.zip

The training dataset is stored inside the data folder.

Rules of submission

  • You have to submit folder after zipped it.

  • The name of the folder is aithon2020_yourname_or_teamname

  • The folder must not contain any dataset.

  • If you have used any python libraries other than in default list, you have to mention in requirements.txt file.

  • There must be aithon_level2.py file inside the folder.

  • And, a function name ‘aithon_level2_api’ must be defined inside the file.

  • The function will be called to train and test your program.

  • The function name, signature and output type is fixed.

  • The first argument is file name that contains data for training.

  • The second argument is file name that contains data for test.

  • The function must return predicted value or emotion for each data in test dataset sequentially in a list. For example, ['sad', 'happy', 'fear', 'fear', ... , 'happy']

By default following, Python libraries are installed in test environment

  • Numpy
  • Pandas
  • OpneCV for Python
  • TensorFlows
  • Keras
  • PyTorch
  • Scikit-learn
  • Theano
  • Matplotlib
  • Seaborn

Importing packages and data

Import necessary packages

In [3]:
!pip install livelossplot
Collecting livelossplot
  Downloading https://files.pythonhosted.org/packages/0f/08/1884157a3de72d41fa97cacacafaa49abf00eba53cb7e08615b2b65b4a9d/livelossplot-0.5.3-py3-none-any.whl
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Installing collected packages: livelossplot
Successfully installed livelossplot-0.5.3
In [4]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import cv2 as cv
import os
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout, BatchNormalization, Activation
from tensorflow.keras import Model,Sequential
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import plot_model
from livelossplot import PlotLossesKeras
from tensorflow.keras.models import load_model
from sklearn.metrics import confusion_matrix
import random
from google.colab import files
/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm

Downloading the data

Downloading dataset.zip

In [5]:
!wget https://spotleai.sgp1.digitaloceanspaces.com/course/zip/aithon2020-level-2.zip
--2020-09-14 04:08:02--  https://spotleai.sgp1.digitaloceanspaces.com/course/zip/aithon2020-level-2.zip
Resolving spotleai.sgp1.digitaloceanspaces.com (spotleai.sgp1.digitaloceanspaces.com)... 103.253.144.208
Connecting to spotleai.sgp1.digitaloceanspaces.com (spotleai.sgp1.digitaloceanspaces.com)|103.253.144.208|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 29149906 (28M) [application/zip]
Saving to: ‘aithon2020-level-2.zip’

aithon2020-level-2. 100%[===================>]  27.80M  11.3MB/s    in 2.5s    

2020-09-14 04:08:07 (11.3 MB/s) - ‘aithon2020-level-2.zip’ saved [29149906/29149906]

Extracting the zip

In [6]:
!unzip aithon2020-level-2.zip
Archive:  aithon2020-level-2.zip
   creating: aithon2020-level-2/
  inflating: aithon2020-level-2/.DS_Store  
  inflating: __MACOSX/aithon2020-level-2/._.DS_Store  
  inflating: aithon2020-level-2/requirements.txt  
  inflating: __MACOSX/aithon2020-level-2/._requirements.txt  
   creating: aithon2020-level-2/source/
   creating: aithon2020-level-2/data/
  inflating: aithon2020-level-2/aithon_level2.py  
   creating: aithon2020-level-2/.idea/
  inflating: aithon2020-level-2/source/classification.py  
  inflating: aithon2020-level-2/data/aithon2020_level2_traning.csv  
  inflating: __MACOSX/aithon2020-level-2/data/._aithon2020_level2_traning.csv  
  inflating: aithon2020-level-2/.idea/workspace.xml  
  inflating: aithon2020-level-2/.idea/untitled.iml  
  inflating: aithon2020-level-2/.idea/modules.xml  
  inflating: aithon2020-level-2/.idea/misc.xml  

Import the training data

In [7]:
train = pd.read_csv('aithon2020-level-2/data/aithon2020_level2_traning.csv')
train.head()
Out[7]:
emotion pixel_0 pixel_1 pixel_2 pixel_3 pixel_4 pixel_5 pixel_6 pixel_7 pixel_8 pixel_9 pixel_10 pixel_11 pixel_12 pixel_13 pixel_14 pixel_15 pixel_16 pixel_17 pixel_18 pixel_19 pixel_20 pixel_21 pixel_22 pixel_23 pixel_24 pixel_25 pixel_26 pixel_27 pixel_28 pixel_29 pixel_30 pixel_31 pixel_32 pixel_33 pixel_34 pixel_35 pixel_36 pixel_37 pixel_38 ... pixel_2264 pixel_2265 pixel_2266 pixel_2267 pixel_2268 pixel_2269 pixel_2270 pixel_2271 pixel_2272 pixel_2273 pixel_2274 pixel_2275 pixel_2276 pixel_2277 pixel_2278 pixel_2279 pixel_2280 pixel_2281 pixel_2282 pixel_2283 pixel_2284 pixel_2285 pixel_2286 pixel_2287 pixel_2288 pixel_2289 pixel_2290 pixel_2291 pixel_2292 pixel_2293 pixel_2294 pixel_2295 pixel_2296 pixel_2297 pixel_2298 pixel_2299 pixel_2300 pixel_2301 pixel_2302 pixel_2303
0 Fear 231 212 156 164 174 138 161 173 182 200 106 38 39 74 138 161 164 179 190 201 210 216 220 224 222 218 216 213 217 220 220 218 217 212 174 160 162 160 139 ... 225 220 215 207 199 167 108 151 122 88 71 84 120 127 105 76 71 78 90 106 123 146 155 148 130 141 119 69 54 89 104 138 152 122 114 101 97 88 110 152
1 Fear 55 55 55 55 55 54 60 68 54 85 151 163 170 179 181 185 188 188 191 196 189 194 198 197 195 194 190 193 195 184 175 172 161 159 158 159 147 136 137 ... 64 90 120 121 127 136 128 150 154 157 155 167 172 174 173 171 167 163 164 160 155 148 148 144 130 126 124 116 118 110 90 83 77 53 37 34 30 34 30 57
2 Sad 20 17 19 21 25 38 42 42 46 54 56 62 63 66 82 108 118 130 139 134 132 126 113 97 126 148 157 161 155 154 154 164 189 204 194 168 180 188 214 ... 63 42 43 76 80 76 73 69 64 59 53 54 52 54 57 60 66 71 77 85 89 93 102 106 105 99 85 62 34 22 29 36 68 91 85 93 97 99 107 118
3 Happy 4 2 13 41 56 62 67 87 95 62 65 70 80 107 127 149 153 150 165 168 177 187 176 167 152 128 130 149 149 146 130 139 139 143 134 105 78 56 36 ... 19 16 11 8 46 47 46 47 48 51 55 55 56 76 85 84 84 77 86 86 80 79 82 84 76 62 57 62 61 62 59 66 80 74 69 99 28 3 7 12
4 Fear 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 ... 43 57 66 74 76 76 82 87 97 72 30 60 68 78 72 63 46 80 80 35 39 48 65 90 89 53 17 37 44 82 94 21 22 51 71 80 82 79 79 83

5 rows × 2305 columns

Generating images from given data

image_from_array() : Return images, when passed an array.

In [8]:
def image_from_array(arr,width,height,return_image=False):
    """
    Input : Takes in an array, width and height
    Output: Displays an image by reshaping image to 
            provided width and height.
    
    
    # Press any key to close the window 
    # if return_image=True, then the image matrix is returned
    # instead of displaying it.
    """
    # Reshaping the given array
    img = np.array(arr.reshape(width,height),dtype=np.uint8)
    
    if return_image:
        return img
    
    else:
        # displaying image ; press any button to close
        cv.imshow('image',img)
        cv.waitKey(0)
        cv.destroyAllWindows()

generate_images(data,labels) : Creates a images folder and stores images

In [ ]:
def generate_images(data,labels):
    """
    Input : Input data matrix of images and lables list
    Output: Store all the images in the /images/ folder 
            along with labels.csv
    """
    # Checking if given data matrix and labels match
    assert len(data)==len(labels),"Input array size ≠ labels size"
    
    # Getting current working directory
    path = os.getcwd()
    label_list = []
    
    try:  
        # Creating a new directory 'images'
        os.mkdir(path+'/images')  
        
    except OSError as error:  
        # If directory already exists
        print(error)
        print("\nDelete the existing images folder & try again")
    store_path = path+'/images/'
    for i in range(len(data)):
        img = image_from_array(data[i],48,48,return_image=True)
        cv.imwrite(store_path+str(i)+'.png',img)
        label_list.append([i,labels[i]])
    label_df = pd.DataFrame(label_list,columns=['Image','Emotion'])
    label_df.to_csv(store_path+'labels.csv',index=False)
In [ ]:
generate_images(train.iloc[:,1:].values,train['emotion'].values)

generate_images_folderwise(x_train,y_train,x_test,y_test)

Creates folders train and test and in each folder class with images
In [9]:
def generate_images_folderwise(x_train,y_train,x_test,y_test):
    """
    Input : Input data matrix of images, lables list 
    Output: Store all the images in the 
            /images/<train or test>/<class_folder> 
    """
    # Checking if given data matrix and labels match
    assert len(x_train)==len(y_train),"Input array size ≠ labels size"
    assert len(x_test)==len(y_test),"Input array size ≠ labels size"
    
    # Getting current working directory
    path = os.getcwd()
    
    temp = 0
    
    try:  
        # Creating a new directory 'images'
        os.mkdir(path+'/images')
        os.mkdir(path+'/images/'+'train')
        os.mkdir(path+'/images/'+'test')
        
    except OSError as error:  
        # If directory already exists
        print(error)
        print("\nDelete the existing images folder & try again")
        
    store_path = path+'/images/'+'train/'
    class_list = np.unique(y_train)
    for j in class_list:
        temp = 0
        os.mkdir(store_path+str(j))
        for i in range(len(x_train)):
            if y_train[i]==j:
                temp += 1
                img = image_from_array(x_train[i],48,48,return_image=True)
                cv.imwrite(store_path+str(j+'/')+str(temp)+'.png',img)
                
    store_path = path+'/images/'+'test/'
    class_list = np.unique(y_test)
    for j in class_list:
        temp = 0
        os.mkdir(store_path+str(j))
        for i in range(len(x_test)):
            if y_test[i]==j:
                temp += 1
                img = image_from_array(x_test[i],48,48,return_image=True)
                cv.imwrite(store_path+str(j+'/')+str(temp)+'.png',img)
In [10]:
x = train.iloc[:,1:].values
y = train['emotion'].values
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, stratify=y,random_state=42)
x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.25,random_state=42)
generate_images_folderwise(x_train,y_train,x_val,y_val)
In [11]:
print("x_train : {0},    y_train : {1}".format(x_train.shape,y_train.shape))
print("x_val   : {0},    y_val   : {1}".format(x_val.shape,y_val.shape))
print("x_test  : {0},    y_test  : {1}".format(x_test.shape,y_test.shape))
x_train : (6489, 2304),    y_train : (6489,)
x_val   : (2164, 2304),    y_val   : (2164,)
x_test  : (2164, 2304),    y_test  : (2164,)

Making a copy of x_test

In [12]:
x_test_copy = x_test

Exploratory data analysis

Checking for class imbalance

In [13]:
temp = train['emotion'].value_counts()
sns.barplot(x=temp.index,y=temp.values)
plt.title("Emotions v/s Number of points belonging to each class\n\n")
plt.xlabel("Emotion")
plt.ylabel("Number of images")
Out[13]:
Text(0, 0.5, 'Number of images')
In [14]:
class_label = train['emotion'].value_counts()
total_points = len(train)
print("Points with class label -> Happy are =  ",class_label.values[0]/total_points*100,"%")
print("Points with class label -> Sad are =  ",class_label.values[1]/total_points*100,"%")
print("Points with class label -> Fear are =  ",class_label.values[2]/total_points*100,"%")
labels = ['Happy','Sad','Fear']
sizes = [44.45,30.23,25.30]
colors = ['yellowgreen', 'gold','blue']
plt.figure(figsize=(6,6))
plt.title("% of points belonging to each class\n\n")
plt.pie(sizes, labels=labels, colors=colors,autopct='%1.1f%%', shadow=True)
Points with class label -> Happy are =   44.45779791069613 %
Points with class label -> Sad are =   30.239437921789776 %
Points with class label -> Fear are =   25.3027641675141 %
Out[14]:
([<matplotlib.patches.Wedge at 0x7f76e61446a0>,
  <matplotlib.patches.Wedge at 0x7f76e6152128>,
  <matplotlib.patches.Wedge at 0x7f76e6152a58>],
 [Text(0.19052115567928468, 1.083375137816366, 'Happy'),
  Text(-0.9067898105676032, -0.6226814911740758, 'Sad'),
  Text(0.7703276824781344, -0.7852358000039648, 'Fear')],
 [Text(0.10392063037051891, 0.5909318933543813, '44.5%'),
  Text(-0.4946126239459653, -0.339644449731314, '30.2%'),
  Text(0.4201787358971642, -0.42831043636579896, '25.3%')])

Exploring various images from the dataset

The referecne for the code below is taken from :
https://www.kaggle.com/xhlulu/eda-simple-keras-cnn-k-mnist

The below code loads 25 random images from dataset, with their label.

In [15]:
plt.figure(figsize=(10,10))
for i in range(25):
    temp = np.random.randint(0,10000)
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(True)
    plt.imshow(image_from_array(train.iloc[temp,1:].values,48,48,return_image=True),cmap="gray")
    plt.xlabel(train['emotion'].values[temp])

Preparing data for modeling

Data Augmentation

In [16]:
path = os.getcwd()

TRAINING_DIR = path+"/images/train/"
training_datagen = ImageDataGenerator(
    rescale = 1./255,
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest')

VALIDATION_DIR = path+"/images/test/"
validation_datagen = ImageDataGenerator(rescale = 1./255)

train_generator = training_datagen.flow_from_directory(
    TRAINING_DIR,
    color_mode='grayscale',
    target_size = (48,48),
    batch_size = 64,
    class_mode='categorical',
    shuffle=True)

validation_generator = validation_datagen.flow_from_directory(
    VALIDATION_DIR,
    color_mode = 'grayscale',
    target_size = (48,48),
    batch_size = 64,
    class_mode='categorical',
    shuffle=False)
Found 6489 images belonging to 3 classes.
Found 2164 images belonging to 3 classes.

Modeling

A simple CNN model

Loading the initial parameters

In [17]:
num_classes = 3
epochs = 100
In [18]:
tf.keras.backend.clear_session()
In [19]:
# Creating the model
model = Sequential()

model.add(Conv2D(32, kernel_size=(5, 5), input_shape=(48,48,1),activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(32, kernel_size=(5, 5),activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2),strides=2))
model.add(Dropout(0.25))


model.add(Conv2D(64, kernel_size=(3, 3),activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(64, kernel_size=(3, 3),activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(3, 3),strides=2))
model.add(Dropout(0.25))

model.add(Conv2D(128, kernel_size=(3, 3),activation='relu'))
model.add(BatchNormalization())
model.add(Conv2D(128, kernel_size=(3, 3),activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(1, 1),strides=2))
model.add(Dropout(0.25))



model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))

model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 44, 44, 32)        832       
_________________________________________________________________
batch_normalization (BatchNo (None, 44, 44, 32)        128       
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 40, 40, 32)        25632     
_________________________________________________________________
batch_normalization_1 (Batch (None, 40, 40, 32)        128       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 20, 20, 32)        0         
_________________________________________________________________
dropout (Dropout)            (None, 20, 20, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 18, 18, 64)        18496     
_________________________________________________________________
batch_normalization_2 (Batch (None, 18, 18, 64)        256       
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 16, 16, 64)        36928     
_________________________________________________________________
batch_normalization_3 (Batch (None, 16, 16, 64)        256       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 7, 7, 64)          0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 7, 7, 64)          0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 5, 5, 128)         73856     
_________________________________________________________________
batch_normalization_4 (Batch (None, 5, 5, 128)         512       
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 3, 3, 128)         147584    
_________________________________________________________________
batch_normalization_5 (Batch (None, 3, 3, 128)         512       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 2, 2, 128)         0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 2, 2, 128)         0         
_________________________________________________________________
flatten (Flatten)            (None, 512)               0         
_________________________________________________________________
dense (Dense)                (None, 512)               262656    
_________________________________________________________________
dropout_3 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 3)                 1539      
=================================================================
Total params: 569,315
Trainable params: 568,419
Non-trainable params: 896
_________________________________________________________________
In [20]:
model.compile(
    loss = 'categorical_crossentropy', 
    optimizer=Adam(), 
    metrics=['accuracy'])
In [21]:
steps_per_epoch = train_generator.n//train_generator.batch_size
validation_steps = validation_generator.n//validation_generator.batch_size
checkpoint = ModelCheckpoint("model_weights.h5", monitor='val_accuracy',
                             save_weights_only=True, mode='max', verbose=1)
callbacks = [PlotLossesKeras(), checkpoint]

history = model.fit(
    x=train_generator,
    steps_per_epoch=steps_per_epoch,
    epochs=epochs,
    validation_data = validation_generator,
    validation_steps = validation_steps,
    callbacks=callbacks
)
accuracy
	training         	 (min:    0.371, max:    0.714, cur:    0.709)
	validation       	 (min:    0.446, max:    0.751, cur:    0.751)
Loss
	training         	 (min:    0.643, max:    1.506, cur:    0.652)
	validation       	 (min:    0.578, max:    1.546, cur:    0.593)

Epoch 00100: saving model to model_weights.h5
101/101 [==============================] - 6s 55ms/step - loss: 0.6519 - accuracy: 0.7091 - val_loss: 0.5931 - val_accuracy: 0.7509

Saving the model

In [22]:
model.save('model')
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0, as updates are applied automatically.
INFO:tensorflow:Assets written to: model/assets
In [23]:
!zip -r model.zip  model
  adding: model/ (stored 0%)
  adding: model/saved_model.pb (deflated 90%)
  adding: model/assets/ (stored 0%)
  adding: model/variables/ (stored 0%)
  adding: model/variables/variables.index (deflated 74%)
  adding: model/variables/variables.data-00000-of-00001 (deflated 8%)
In [24]:
files.download('model.zip') 

Evaluating the model

In [25]:
x_test = x_test.astype('float32')/255.0
In [26]:
train_generator.class_indices
Out[26]:
{'Fear': 0, 'Happy': 1, 'Sad': 2}
In [27]:
def evaluate(x_test,return_misclassified=False,y_test=[]):
  output = []
  misclassified_indices = []
  count = 0 
  temp = 0
  for i in range(len(x_test)):
    temp = np.argmax(model.predict(x_test[i].reshape(1,48,48,1)),axis=1)
    if temp ==0:
      output.append('Fear')
    elif temp==1:
      output.append('Happy')
    elif temp==2:
      output.append('Sad')
    if return_misclassified:
      if output[i]!=y_test[i]:
        misclassified_indices.append(i)
  if return_misclassified:
    return output,misclassified_indices
  else:
    return output
In [28]:
output = evaluate(x_test)
In [29]:
print("Accuracy on unseen data : ",np.sum(output==y_test)/len(x_test))
Accuracy on unseen data :  0.7467652495378928

Understanding the model in depth

Where the model is going wrong

Getting points from data on which model is not working

In [30]:
output, id = evaluate(x_test,return_misclassified=True,y_test=y_test)
In [31]:
#plt.subplot_tool() -> Use this if you are running in local jupyter-notebook and not colab
plt.figure(figsize=(10,12))
for i in range(25):
    temp = random.sample(id,1)[0]
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(True)
    plt.imshow(image_from_array(x_test_copy[temp],48,48,return_image=True),cmap="gray")
    plt.xlabel("Actual :"+y_test[temp]+'\n'+"Predicted :"+output[temp])