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trainmodel.py
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trainmodel.py
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from keras.utils import to_categorical
from keras_preprocessing.image import load_img
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D
import os
import pandas as pd
import numpy as np
TRAIN_DIR = 'images/train'
TEST_DIR = 'images/test'
def createdataframe(dir):
image_paths = []
labels = []
for label in os.listdir(dir):
for imagename in os.listdir(os.path.join(dir,label)):
image_paths.append(os.path.join(dir,label,imagename))
labels.append(label)
print(label, "completed")
return image_paths,labels
train = pd.DataFrame()
train['image'], train['label'] = createdataframe(TRAIN_DIR)
print(train)
test = pd.DataFrame()
test['image'], test['label'] = createdataframe(TEST_DIR)
print(test)
print(test['image'])
from tqdm.notebook import tqdm
def extract_features(images):
features = []
for image in tqdm(images):
img = load_img(image,grayscale = True )
img = np.array(img)
features.append(img)
features = np.array(features)
features = features.reshape(len(features),48,48,1)
return features
train_features = extract_features(train['image'])
test_features = extract_features(test['image'])
x_train = train_features/255.0
x_test = test_features/255.0
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(train['label'])
y_train = le.transform(train['label'])
y_test = le.transform(test['label'])
y_train = to_categorical(y_train,num_classes = 7)
y_test = to_categorical(y_test,num_classes = 7)
model = Sequential()
# convolutional layers
model.add(Conv2D(128, kernel_size=(3,3), activation='relu', input_shape=(48,48,1)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.4))
model.add(Conv2D(256, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.4))
model.add(Conv2D(512, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.4))
model.add(Conv2D(512, kernel_size=(3,3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.4))
model.add(Flatten())
# fully connected layers
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.3))
# output layer
model.add(Dense(7, activation='softmax'))
model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = 'accuracy' )
model.fit(x= x_train,y = y_train, batch_size = 128, epochs = 100, validation_data = (x_test,y_test))
model_json = model.to_json()
with open("emotionsdetector.json",'w') as json_file:
json_file.write(model_json)
model.save("emotionsdetector.h5")
from keras.models import model_from_json
# json file and h5 file will be created
json_file = open("emotionsdetector.json", "r")
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)
model.load_weights("emotionsdetector.h5")
#here a h5 file will be created which will be needed in the future oparation
label = ['angry','disgust','fear','happy','neutral','sad','surprise']
def ef(image):
img = load_img(image,grayscale = True )
feature = np.array(img)
feature = feature.reshape(1,48,48,1)
return feature/255.0
import matplotlib.pyplot as plt
#%matplotlib inline
image = 'images/train/sad/42.jpg'
print("original image is of sad")
img = ef(image)
pred = model.predict(img)
pred_label = label[pred.argmax()]
print("model prediction is ",pred_label)
plt.imshow(img.reshape(48,48),cmap='gray')
#-----------------------------
image = 'images/train/fear/2.jpg'
print("original image is of sad")
img = ef(image)
pred = model.predict(img)
pred_label = label[pred.argmax()]
print("model prediction is ",pred_label)
plt.imshow(img.reshape(48,48),cmap='gray')
#-----------------------------
image = 'images/train/disgust/299.jpg'
print("original image is of disgust")
img = ef(image)
pred = model.predict(img)
pred_label = label[pred.argmax()]
print("model prediction is ",pred_label)
plt.imshow(img.reshape(48,48),cmap='gray')