import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from matplotlib import pyplot as plt
import cv2
from tqdm import tqdm
import multiprocessing as mp
tqdm.pandas(desc="my bar!")
def image_rotate(img, angle):
"""
Image rotation at certain angle. It is used for data augmentation
"""
rows, cols, _ = img.shape
M = cv2.getRotationMatrix2D((cols/2, rows/2), angle, 1)
dst = cv2.warpAffine(img, M, (cols, rows))
return np.expand_dims(dst, 0)
def read_alphabets(alphabet_directory, directory):
"""
Reads all the characters from alphabet_directory and augment each image with 90, 180, 270 degrees of rotation.
"""
datax = None
datay = []
characters = os.listdir(alphabet_directory)
for character in characters:
images = os.listdir(alphabet_directory + character + '/')
for img in images:
image = cv2.resize(cv2.imread(
alphabet_directory + character + '/' + img), (28, 28))
image90 = image_rotate(image, 90)
image180 = image_rotate(image, 180)
image270 = image_rotate(image, 270)
image = np.expand_dims(image, 0)
if datax is None:
datax = np.vstack([image, image90, image180, image270])
else:
datax = np.vstack([datax, image, image90, image180, image270])
datay.append(directory + '_' + character + '_0')
datay.append(directory + '_' + character + '_90')
datay.append(directory + '_' + character + '_180')
datay.append(directory + '_' + character + '_270')
return datax, np.array(datay)
def read_images(base_directory):
"""
Used multithreading for data reading to decrease the reading time drastically
"""
datax = None
datay = []
pool = mp.Pool(mp.cpu_count())
results = [pool.apply(read_alphabets, args=(
base_directory + '/' + directory + '/', directory, )) for directory in os.listdir(base_directory)]
pool.close()
for result in results:
if datax is None:
datax = result[0]
datay = result[1]
else:
datax = np.vstack([datax, result[0]])
datay = np.concatenate([datay, result[1]])
return datax, datay
