| 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) |
