o Ni@sdZddlmZddlmZddlmZddlZddlZddlm m Z ddl m ZdZdZdZd Zd Zd ZGd d d ejjZdS)zCaltech images dataset.)absolute_import)division)print_functionNa0@article{FeiFei2004LearningGV, title={Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories}, author={Li Fei-Fei and Rob Fergus and Pietro Perona}, journal={Computer Vision and Pattern Recognition Workshop}, year={2004}, } aCaltech-101 consists of pictures of objects belonging to 101 classes, plus one `background clutter` class. Each image is labelled with a single object. Each class contains roughly 40 to 800 images, totalling around 9k images. Images are of variable sizes, with typical edge lengths of 200-300 pixels. This version contains image-level labels only. The original dataset also contains bounding boxes. z*image_classification/caltech101_labels.txtz8http://www.vision.caltech.edu/Image_Datasets/Caltech101/z101_ObjectCategories.tar.gzc@s6eZdZdZejddZddZddZ dd Z d S) Caltech101z Caltech-101.z3.0.0z6New split API (https://tensorflow.org/datasets/splits)c CsJtjt}tjj|ttjtjtjj |dtj ddt t dS)N) names_fileimagelabelzimage/file_name)r r )builder descriptionfeaturessupervised_keyshomepagecitation) tfdscore get_tfds_path _LABELS_FNAME DatasetInfo _DESCRIPTIONr FeaturesDictImage ClassLabelText_URL _CITATION)selfrrd/home/ubuntu/.local/lib/python3.10/site-packages/tensorflow_datasets/image_classification/caltech.py_info8s  zCaltech101._infocCsH|tjtt}tjjtj j |dddtjjtj j |dddgS)NT)images_dir_pathis_train_split)name gen_kwargsF) download_and_extractospathjoinr _IMAGES_FNAMErrSplitGeneratorSplitTRAINTEST)r dl_managerr'rrr_split_generatorsGszCaltech101._split_generatorsccs.tj}tjdtjj|d}tj ||}tjj|}|D]i}tjj tj ||rtjj tj ||D]N\}} } t t| krUtdt t| |tjj| t dd} t| | } |ri| n| } | D]}|drtj ||}|||d}d||f|fVqmq?q%tj|d S) aLGenerates images and labels given the image directory path. As is usual for this dataset, 30 random examples from each class are added to the train split, and the remainder are added to the test split. Args: images_dir_path: path to the directory where the images are stored. is_train_split: bool, if true, generates the train split, else generates the test split. Yields: The image path, and its corresponding label and filename. Raises: ValueError: If too few points are present to create the train set for any class. irz%Fewer than {} ({}) points in class {}F)replacez.jpgrz%s/%sN)nprandom get_stateseedtfiogfilelistdirr&r'r(isdirwalk_TRAIN_POINTS_PER_CLASSlen ValueErrorformatchoiceset differenceendswithlower set_state)rr!r"numpy_original_state parent_dirwalk_dirdirsd full_path_fnames train_fnames test_fnamesfnames_to_emit image_file image_pathrecordrrr_generate_examplesXs<  "      zCaltech101._generate_examplesN) __name__ __module__ __qualname____doc__rrVersionVERSIONr r/rSrrrrr2s r)rW __future__rrrr&numpyr1tensorflow.compat.v2compatv2r5tensorflow_datasets.public_api public_apirrrrrr)r;rGeneratorBasedBuilderrrrrrs