rwchakra

['\n', ' ', '!', "'", '(', ')', '*', ',', '-', '.', '0', 
 '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', 
 '?', '\\', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 
 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 
 'u', 'v', 'w', 'x', 'y', 'z', '{', '}']

rwchakra

to_remove = {'\n', '\\','{', '}'}

new_data = ''.join(char for char in data if char not in to_remove)

unique_chars = sorted(list(set(new_data)))

'''
[' ', '!',"'",'(', ')', '*', ',', '-', '.', '0', '1', '2', '3',
 '4', '5', '6', '7', '8', '9', ':', ';', '?', 'a', 'b', 'c', 'd',
 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q',

rwchakra

with open('bible.txt.rtf', encoding = 'utf-8', errors = 'ignore') as f:
  data = f.read()
  
data = data.lower()

print(data[500:550], data[0:65])
'''
('ed the heaven and the earth.\\\n\\\n1:2 and the earth ',
 '{\\rtf1\\ansi\\ansicpg1252\\cocoartf1671\\cocoasubrtf500\n{\\fonttbl\\f0\\')
'''

rwchakra

from keras.callbacks import ModelCheckpoint
from keras.callbacks import CSVLogger
from keras.callbacks import EarlyStopping
from keras.optimizers import Adam

import model

NO_OF_TRAINING_IMAGES = len(os.listdir('/your_data/train_frames/train/'))
NO_OF_VAL_IMAGES = len(os.listdir('/your_data/val_frames/val/'))

rwchakra

import cv2

def data_gen(img_folder, mask_folder, batch_size):
  c = 0
  n = os.listdir(img_folder) #List of training images
  random.shuffle(n)
  
  while (True):
    img = np.zeros((batch_size, 512, 512, 3)).astype('float')
    mask = np.zeros((batch_size, 512, 512, 1)).astype('float')

rwchakra

train_image_generator = train_datagen.flow_from_directory(
'data/train_frames/train',
batch_size = #NORMALLY 4/8/16/32)

train_mask_generator = train_datagen.flow_from_directory(
'data/train_masks/train',
batch_size = #NORMALLY 4/8/16/32)

val_image_generator = val_datagen.flow_from_directory(
'data/val_frames/val',

rwchakra

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)
        
val_datagen = ImageDataGenerator(rescale=1./255)

rwchakra

import os
import random
import re
from PIL import Image

DATA_PATH = '/path/to/your/data_dir'
FRAME_PATH = DATA_PATH+'/frames'
MASK_PATH = DATA_PATH+'/masks'

rwchakra

subjects = ['English', 'Physics', 'Math', 'Chemistry', 'Economics']
grades = [85, 91, 90, 90, 95]
marks = {}

performance = zip(subjects, grades)

print performance #-> [('English', 85), ('Physics', 91), ('Math', 90), ('Chemistry', 90), ('Economics', 95)]

marks = dict(performance)

rwchakra

# -*- coding: utf-8 -*-

sentence = "There will be a full moon tonight. It’s going to be beautiful!"
vowel_count = {}

vowels = ['a', 'e', 'i', 'o', 'u']

for char in sentence:
    if char in vowels:
        vowel_count[char] = vowel_count.get(char, 0) + 1