Prasad9

let uploadQuestionFirebaseRef = FIRDatabase.database().reference().child(<your_channel_name>)
uploadQuestionFirebaseRef.setValue(<upload_data>)

Prasad9

let questionListenerFirebaseRef = FIRDatabase.database().reference().child(<your_channel_name>)
questionListenerFirebaseRef.observeEventType(FIRDataEventType.ChildAdded) { (dataSnapshot: FIRDataSnapshot) in
	// Question added. Proceed accordingly.
}
            

Prasad9

import tensorflow as tf
import matplotlib.image as mpimg
import numpy as np

IMAGE_SIZE = 224

def tf_resize_images(X_img_file_paths):
    X_data = []
    tf.reset_default_graph()
    X = tf.placeholder(tf.float32, (None, None, 3))

Prasad9

def central_scale_images(X_imgs, scales):
    # Various settings needed for Tensorflow operation
    boxes = np.zeros((len(scales), 4), dtype = np.float32)
    for index, scale in enumerate(scales):
        x1 = y1 = 0.5 - 0.5 * scale # To scale centrally
        x2 = y2 = 0.5 + 0.5 * scale
        boxes[index] = np.array([y1, x1, y2, x2], dtype = np.float32)
    box_ind = np.zeros((len(scales)), dtype = np.int32)
    crop_size = np.array([IMAGE_SIZE, IMAGE_SIZE], dtype = np.int32)
    

Prasad9

from math import ceil, floor

def get_translate_parameters(index):
    if index == 0: # Translate left 20 percent
        offset = np.array([0.0, 0.2], dtype = np.float32)
        size = np.array([IMAGE_SIZE, ceil(0.8 * IMAGE_SIZE)], dtype = np.int32)
        w_start = 0
        w_end = int(ceil(0.8 * IMAGE_SIZE))
        h_start = 0
        h_end = IMAGE_SIZE

Prasad9

def rotate_images(X_imgs):
    X_rotate = []
    tf.reset_default_graph()
    X = tf.placeholder(tf.float32, shape = (IMAGE_SIZE, IMAGE_SIZE, 3))
    k = tf.placeholder(tf.int32)
    tf_img = tf.image.rot90(X, k = k)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for img in X_imgs:
            for i in range(3):  # Rotation at 90, 180 and 270 degrees

Prasad9

def flip_images(X_imgs):
    X_flip = []
    tf.reset_default_graph()
    X = tf.placeholder(tf.float32, shape = (IMAGE_SIZE, IMAGE_SIZE, 3))
    tf_img1 = tf.image.flip_left_right(X)
    tf_img2 = tf.image.flip_up_down(X)
    tf_img3 = tf.image.transpose_image(X)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for img in X_imgs:

Prasad9

from math import pi

def rotate_images(X_imgs, start_angle, end_angle, n_images):
    X_rotate = []
    iterate_at = (end_angle - start_angle) / (n_images - 1)
    
    tf.reset_default_graph()
    X = tf.placeholder(tf.float32, shape = (None, IMAGE_SIZE, IMAGE_SIZE, 3))
    radian = tf.placeholder(tf.float32, shape = (len(X_imgs)))
    tf_img = tf.contrib.image.rotate(X, radian)

Prasad9

def add_salt_pepper_noise(X_imgs):
    # Need to produce a copy as to not modify the original image
    X_imgs_copy = X_imgs.copy()
    row, col, _ = X_imgs_copy[0].shape
    salt_vs_pepper = 0.2
    amount = 0.004
    num_salt = np.ceil(amount * X_imgs_copy[0].size * salt_vs_pepper)
    num_pepper = np.ceil(amount * X_imgs_copy[0].size * (1.0 - salt_vs_pepper))
    
    for X_img in X_imgs_copy:

Prasad9

import cv2

def add_gaussian_noise(X_imgs):
    gaussian_noise_imgs = []
    row, col, _ = X_imgs[0].shape
    # Gaussian distribution parameters
    mean = 0
    var = 0.1
    sigma = var ** 0.5