Datasets:
File size: 2,273 Bytes
a5a3744 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | import os
from PIL import Image
def fetching_selected_data(test_split: int, max_len: int = 1000000000):
cats_images_path = r"/content/data/PetImages/Cat"
dogs_images_path = r"/content/data/PetImages/Dog"
total_len = min(len(os.listdir(cats_images_path)), len(os.listdir(dogs_images_path)), max_len)
training_set_count = int(total_len * (1 - (test_split/100)))
testing_set_count = total_len - training_set_count
def _get_image_files(parent_dir: str, first_count: int, max_count: int):
all_files = []
count = 0
for child in os.listdir(parent_dir):
grandchild = os.path.join(parent_dir, child)
if not os.path.isdir(grandchild):
count += 1
if os.path.isdir(grandchild):
continue
final_full_path = os.path.join(parent_dir, grandchild)
all_files.append(final_full_path)
else:
_get_image_files(grandchild)
last_count = first_count + max_count
return all_files[first_count:last_count]
def _get_input_labels(first_count: int, max_count: int):
cats_list = _get_image_files(cats_images_path, first_count=first_count, max_count=max_count)
dogs_list = _get_image_files(dogs_images_path, first_count=first_count, max_count=max_count)
img_files_list = cats_list + dogs_list
X, y = [], []
for file_path in img_files_list:
img_file_data = Image.open(file_path).convert("RGB")
X.append(img_file_data)
if 'cat' in file_path.lower():
y.append(0)
elif 'dog' in file_path.lower():
y.append(1)
return X, y
train_first_count = 0
train_max_count = training_set_count
X_train, y_train = _get_input_labels(train_first_count, train_max_count)
test_first_count = training_set_count
test_max_count = testing_set_count
X_test, y_test = _get_input_labels(test_first_count, test_max_count)
return X_train, X_test, y_train, y_test
(fetched_X_train, fetched_X_test,
fetched_y_train, fetched_y_test) = fetching_selected_data(test_split=0.25, max_len=5000) |