emotion_detection_(mp).py

# -*- coding: utf-8 -*-
"""Emotion Detection (MP)

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1aGyVngGwmSOWjSuUKKjOsl5Oh3BSleqQ
"""

import numpy as np
import pandas as pd
from PIL import Image
from tqdm import tqdm
import os

# convert string to integer
def atoi(s):
    n = 0
    for i in s:
        n = n*10 + ord(i) - ord("0")
    return n

# making folders
outer_names = ['test','train']
inner_names = ['angry', 'disgusted', 'fearful', 'happy', 'sad', 'surprised', 'neutral']
os.makedirs('data', exist_ok=True)
for outer_name in outer_names:
    os.makedirs(os.path.join('data',outer_name), exist_ok=True)
    for inner_name in inner_names:
        os.makedirs(os.path.join('data',outer_name,inner_name), exist_ok=True)

# to keep count of each category
angry = 0
disgusted = 0
fearful = 0
happy = 0
sad = 0
surprised = 0
neutral = 0
angry_test = 0
disgusted_test = 0
fearful_test = 0
happy_test = 0
sad_test = 0
surprised_test = 0
neutral_test = 0

df = pd.read_csv('./fer2013.csv')
mat = np.zeros((48,48),dtype=np.uint8)
print("Saving images...")

# read the csv file line by line
for i in tqdm(range(len(df))):
    txt = df['pixels'][i]
    words = txt.split()

    # the image size is 48x48
    for j in range(2304):
        xind = j // 48
        yind = j % 48
        mat[xind][yind] = atoi(words[j])

    img = Image.fromarray(mat)

    # train
    if i < 28709:
        if df['emotion'][i] == 0:
            img.save('train/angry/im'+str(angry)+'.png')
            angry += 1
        elif df['emotion'][i] == 1:
            img.save('train/disgusted/im'+str(disgusted)+'.png')
            disgusted += 1
        elif df['emotion'][i] == 2:
            img.save('train/fearful/im'+str(fearful)+'.png')
            fearful += 1
        elif df['emotion'][i] == 3:
            img.save('train/happy/im'+str(happy)+'.png')
            happy += 1
        elif df['emotion'][i] == 4:
            img.save('train/sad/im'+str(sad)+'.png')
            sad += 1
        elif df['emotion'][i] == 5:
            img.save('train/surprised/im'+str(surprised)+'.png')
            surprised += 1
        elif df['emotion'][i] == 6:
            img.save('train/neutral/im'+str(neutral)+'.png')
            neutral += 1

    # test
    else:
        if df['emotion'][i] == 0:
            img.save('test/angry/im'+str(angry_test)+'.png')
            angry_test += 1
        elif df['emotion'][i] == 1:
            img.save('test/disgusted/im'+str(disgusted_test)+'.png')
            disgusted_test += 1
        elif df['emotion'][i] == 2:
            img.save('test/fearful/im'+str(fearful_test)+'.png')
            fearful_test += 1
        elif df['emotion'][i] == 3:
            img.save('test/happy/im'+str(happy_test)+'.png')
            happy_test += 1
        elif df['emotion'][i] == 4:
            img.save('test/sad/im'+str(sad_test)+'.png')
            sad_test += 1
        elif df['emotion'][i] == 5:
            img.save('test/surprised/im'+str(surprised_test)+'.png')
            surprised_test += 1
        elif df['emotion'][i] == 6:
            img.save('test/neutral/im'+str(neutral_test)+'.png')
            neutral_test += 1

print("Done!")

import numpy as np
import argparse
import matplotlib.pyplot as plt
import cv2
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# command line argument
ap = argparse.ArgumentParser()
ap.add_argument("--mode",help="train/display")
mode = ap.parse_args().mode

# plots accuracy and loss curves
def plot_model_history(model_history):
    """
    Plot Accuracy and Loss curves given the model_history
    """
    fig, axs = plt.subplots(1,2,figsize=(15,5))
    # summarize history for accuracy
    axs[0].plot(range(1,len(model_history.history['accuracy'])+1),model_history.history['accuracy'])
    axs[0].plot(range(1,len(model_history.history['val_accuracy'])+1),model_history.history['val_accuracy'])
    axs[0].set_title('Model Accuracy')
    axs[0].set_ylabel('Accuracy')
    axs[0].set_xlabel('Epoch')
    axs[0].set_xticks(np.arange(1,len(model_history.history['accuracy'])+1),len(model_history.history['accuracy'])/10)
    axs[0].legend(['train', 'val'], loc='best')
    # summarize history for loss
    axs[1].plot(range(1,len(model_history.history['loss'])+1),model_history.history['loss'])
    axs[1].plot(range(1,len(model_history.history['val_loss'])+1),model_history.history['val_loss'])
    axs[1].set_title('Model Loss')
    axs[1].set_ylabel('Loss')
    axs[1].set_xlabel('Epoch')
    axs[1].set_xticks(np.arange(1,len(model_history.history['loss'])+1),len(model_history.history['loss'])/10)
    axs[1].legend(['train', 'val'], loc='best')
    fig.savefig('plot.png')
    plt.show()


train_dir = 'data/train'
val_dir = 'data/test'

num_train = 28709
num_val = 7178
batch_size = 64
num_epoch = 50

train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        train_dir,
        target_size=(48,48),
        batch_size=batch_size,
        color_mode="grayscale",
        class_mode='categorical')

validation_generator = val_datagen.flow_from_directory(
        val_dir,
        target_size=(48,48),
        batch_size=batch_size,
        color_mode="grayscale",
        class_mode='categorical')

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

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

model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(7, activation='softmax'))

# If you want to train the same model or try other models, go for this
if mode == "train":
    model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.0001, decay=1e-6),metrics=['accuracy'])
    model_info = model.fit_generator(
            train_generator,
            steps_per_epoch=num_train // batch_size,
            epochs=num_epoch,
            validation_data=validation_generator,
            validation_steps=num_val // batch_size)
    plot_model_history(model_info)
    model.save_weights('model.h5')

# emotions will be displayed on your face from the webcam feed
elif mode == "display":
    model.load_weights('model.h5')

    # prevents openCL usage and unnecessary logging messages
    cv2.ocl.setUseOpenCL(False)

    # dictionary which assigns each label an emotion (alphabetical order)
    emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}

    # start the webcam feed
    cap = cv2.VideoCapture(0)
    while True:
        # Find haar cascade to draw bounding box around face
        ret, frame = cap.read()
        if not ret:
            break
        facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = facecasc.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5)

        for (x, y, w, h) in faces:
            cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (255, 0, 0), 2)
            roi_gray = gray[y:y + h, x:x + w]
            cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0)
            prediction = model.predict(cropped_img)
            maxindex = int(np.argmax(prediction))
            cv2.putText(frame, emotion_dict[maxindex], (x+20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)

        cv2.imshow('Video', cv2.resize(frame,(1600,960),interpolation = cv2.INTER_CUBIC))
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()