anuj2110

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def get_weights(total_data):
    '''
    function for initialize random values in the weight vectors for the neural network to be used.
    uses the no of features to initialize a vector.
    '''
    y = np.random.random()*(2.0/np.sqrt(total_data))

anuj2110

import 'dart:async';

class Cake {}

class Order {
  String type;
  Order(this.type);
}

void main() {

anuj2110

import 'dart:html';

void main(){
  
  final ButtonElement button = querySelector('button');
  
  button.onClick
    .timeout(
          
        new Duration(seconds:1),

anuj2110

import pandas as pd
import numpy as np

def loadDataFromCSV(path="fer2013/fer2013.csv"):
    df = pd.read_csv(path) #readeing the csv
    pixels = df["pixels"].tolist() #extracting the pixels column
    emotions = pd.get_dummies(df["emotion"]).as_matrix() #extracting emotions and turning them into one-hot labels
    faces =  [] #list for storing faces
    for pixel_sequence  in tqdm(pixels): #main loop for turning the pixel value to images and storing them as numpy arrays
        face = [int(pixel) for pixel in pixel_sequence.split(" ")]

anuj2110

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D,MaxPooling2D,BatchNormalization,Dropout,Flatten,Dense

num_features = 64
num_labels=7
model = Sequential()
model.add(Conv2D(num_features, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 1), kernel_regularizer=l2(0.01)))
model.add(Conv2D(num_features, kernel_size=(3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

anuj2110

import cv2 
from tensorflow.keras.models import load_model
import numpy as np

cascade_file = "haarcascade_frontalface_default.xml"
model_file = "fermodel.h5"
emotions = ["angry","disgust","scared", "happy", "sad", "surprised","neutral"]

face_detection = cv2.CascadeClassifier(cascade_file)
emotion_classifier = load_model(model_file, compile=False)

anuj2110

import cv2
from tensorflow.keras.models import load_model
import matplotlib.pyplot as plt
import numpy as np
import os
from imutils import build_montages

haar_cascade = "haarcascade_frontalface_default.xml"
imgs = os.listdir(path_to_images_dir)
model_name = path_to_model

anuj2110

# Making the imports
import matplotlib.pyplot as plt 
import numpy as np
import cv2
import os
from imutils import build_montages

def plot_distribution(paths:list,portion:str):
    """
    This function is used to plot the distribution of train and test images among NORMAL and PNEUMONIA labels

anuj2110

# making the imports
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Input,Conv2D,MaxPooling2D,Dropout,Flatten,Dense,Activation,BatchNormalization,add
from tensorflow.keras.models  import Model,Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import plot_model
from tensorflow.keras.applications.vgg16 import VGG16,preprocess_input
import os

anuj2110

# we make our imports
from flask import Flask,render_template,redirect,request,send_from_directory
from tensorflow.keras.models import load_model
import os
from PIL import Image
import numpy as np

# we load the model before starting the app
model_file = "model.h5"
model = load_model(model_file)