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import cv2 # working with, mainly resizing, images
import numpy as np # dealing with arrays
import os # dealing with directories
from random import shuffle # mixing up or currently ordered data that might lead our network astray in training.
from tqdm import tqdm # a nice pretty percentage bar for tasks. Thanks to viewer Daniel Bühler for this suggestion
from catdog_config import IMG_SIZE
def label_img(img):
word_label = img.split('.')[-3]
# conversion to one-hot array [cat,dog]
# [much cat,
if word_label == 'cat': return [1,0]
# [no cat, v
elif word_label == 'dog': return [0,1]
# TODO: encode image size in file name
def create_train_data(train_dir = None, load_from_disk=False):
if load_from_disk:
training_data = np.load('train_data.npy')
training_data = []
for img in tqdm(os.listdir(train_dir)):
label = label_img(img)
path = os.path.join(train_dir,img)
img = cv2.imread(path,cv2.IMREAD_COLOR)
img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
shuffle(training_data)'train_data.npy', training_data)
return training_data
def process_test_data(test_dir=None, load_from_disk=False):
if load_from_disk:
testing_data = np.load('test_data.npy')
testing_data = []
for img in tqdm(os.listdir(test_dir)):
path = os.path.join(test_dir,img)
img_num = img.split('.')[0]
img = cv2.imread(path,cv2.IMREAD_COLOR)
img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
testing_data.append([np.array(img), img_num])
shuffle(testing_data)'test_data.npy', testing_data)
return testing_data