smartdoc15-ch1 0.8

Dataset version

The version of the dataset used by this wrapper is: 2.0.0.

The source for the dataset is here: https://github.com/jchazalon/smartdoc15-ch1-dataset

Sample usage

There are 3 tasks this Python package helps you to test your methods against, but first of all you have to properly install this package:

pip install smartdoc_ch1

A good practice is to install such package in a virtual environment. We recommend to use Virtualenv Wrapper to use virtual environments.

Manual download option

If you are behind a proxy, have a slow connexion or for any other reason, you may want to download the dataset manually instead of letting the Python wrapper do it for you. This is simple:

1. download the frames.tar.gz and models.tar.gz files from https://github.com/jchazalon/smartdoc15-ch1-dataset/releases to some local directory;

2. choose where you want to store the files and manually create the file hierarchy (the smartdoc_ch1_home intermediate directory is important here):

mkdir -p PATH_TO_STORAGE_DIR/smartdoc_ch1_home/frames
mkdir -p PATH_TO_STORAGE_DIR/smartdoc_ch1_home/models

3. extract the archives to their target directories:

tar -xzf PATH_TO_FRAMES.TAR.GZ -C PATH_TO_STORAGE_DIR/smartdoc_ch1_home/frames
tar -xzf PATH_TO_MODELS.TAR.GZ -C PATH_TO_STORAGE_DIR/smartdoc_ch1_home/models

Then, make sure you specify data_home=PATH_TO_STORAGE_DIR and download_if_missing=False when you call the load_sd15ch1_frames and load_sd15ch1_models functions. The functions get_sd15ch1_basedir_frames and get_sd15ch1_basedir_models also require that you specify data_home=PATH_TO_STORAGE_DIR.

By default, the path to local dataset storage complies with Scikit-learn standard location: PATH_TO_STORAGE_DIR=~/scikit_learn_data

API

TODO DOC

MODEL_VARIANT_01_ORIGINAL = "01-original"
MODEL_VARIANT_02_EDITED = "02-edited"
MODEL_VARIANT_03_CAPTURED = "03-captured-nexus"
MODEL_VARIANT_04_CORRECTED = "04-corrected-nexus"
MODEL_VARIANT_05_SCALED33 = "05-corrected-nexus-scaled33"

load_sd15ch1_frames(data_home=None,
                        sample=1.0,
                        shuffle=False,
                        random_state=0,
                        download_if_missing=True,
                        load_images=False,
                        resize=None,
                        color=False,
                        with_model_classif_targets=True,
                        with_modeltype_classif_targets=True,
                        with_segmentation_targets=True,
                        with_model_shapes=True,
                        return_X_y=False,
                        )

load_sd15ch1_models(data_home=None,
                        download_if_missing=True,
                        load_images=False,
                        variant=MODEL_VARIANT_05_SCALED33,
                        color=False,
                        with_model_ids=True,
                        with_modeltype_ids=True,
                        return_X_y=False,
                        )

read_sd15ch1_image(root_dir,
                       image_relative_path,
                       resize=None,
                       color=False)

read_sd15ch1_images(root_dir,
                        image_relative_path_seq,
                        resize=None,
                        color=False)

get_sd15ch1_basedir_frames(data_home=None)

get_sd15ch1_basedir_models(data_home=None)

eval_sd15ch1_segmentations(segmentations,
                           target_segmentations,
                           model_shapes,
                           frame_resize_factor=1.0,
                           print_summary=False)

eval_sd15ch1_classifications(labels,
                             target_labels)