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app.py
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app.py
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from flask import Flask, render_template,request
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.optimizers import adam_v2
from tensorflow.keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
app = Flask(__name__)
#specify the path to the train/test folders
train_dir = 'data/train/'
val_dir = 'data/test/'
#set image pixels to value of 1 or 0
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range = 10,
horizontal_flip=True,
width_shift_range=0.1,
height_shift_range=0.1,
fill_mode = 'nearest')
val_datagen = ImageDataGenerator(rescale=1./255)
#set image size/color/class for training and validation
train_generator = train_datagen.flow_from_directory(
train_dir,
#images in FER-2013 dataset are grayscale and 48x48
target_size=(48,48),
color_mode="grayscale",
class_mode='binary')
validation_generator = val_datagen.flow_from_directory(
val_dir,
target_size=(48,48),
color_mode="grayscale",
class_mode='binary')
model = Sequential()
model.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu', input_shape=(48,48,1)))
model.add(Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Dropout(0.3))
model.add(Conv2D(128, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(128, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
model.add(Conv2D(256, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(256, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
model.add(Conv2D(512, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(Conv2D(512, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=adam_v2.Adam(learning_rate=0.0001),metrics=['accuracy'])
model_info = model.fit(
train_generator,
steps_per_epoch=12045// 64,
epochs=100,
validation_data=validation_generator,
validation_steps= 3021 // 64)
model.save('kargocharlie.h5')
import cv2
@app.route('/')
def index():
return render_template('home.html')
@app.route('/pred', methods=['GET', 'POST'])
def pred():
if request.method == 'POST':
# store image in static folder
f = request.files['process_image']
f.save('./static/process_image.jpg')
# read image from static folder
img = cv2.imread('./static/process_image.jpg')
# resize image to 48x48
img = cv2.resize(img, (48, 48))
# convert image to grayscale
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# img = image.load_img("{{url_for('static',filename = 'images/download.jpg')}}",target_size = (48,48),color_mode = "grayscale")
img = np.array(img)
label_dict = {0:'happy',1:'sad'}
img = np.expand_dims(img,axis = 0) #makes image shape (1,48,48)
img = img.reshape(1,48,48,1)
result = model.predict(img)
result = list(result[0])
if result[0] == 1:
print("sad")
return render_template('sad.html')
else:
print("happy")
return render_template('happy.html')
if __name__ == '__main__':
app.debug = True
app.run()