EmotionDetector/edModel.py at master · ArchitGargPro/EmotionDetector · GitHub

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import numpy as np
import argparse
import cv2
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv2D
from keras.optimizers import Adam
from keras.layers.pooling import MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import matplotlib as mpl
mpl.use('TkAgg')
import matplotlib.pyplot as plt

mode = "display"

#   Uncomment below code to train your custom model

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

# 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['acc'])+1),model_history.history['acc'])
#     axs[0].plot(range(1,len(model_history.history['val_acc'])+1),model_history.history['val_acc'])
#     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['acc'])+1),len(model_history.history['acc'])/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()

# # Define data generators
# 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')

# Create the model
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(True)

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

    # 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()