Data Readers

Data Reader is a function for reading input data. You can use 2 ways to set data_reader for dataset:

  • Define reader as a string.
reader: opencv_imread
  • Define reader as a dictionary, using type: for setting reader name. This approach gives opportunity to set additional parameters for adapter if it is required.
type: opencv_imread
reading_flag: gray

In case, when you have model with several inputs which should use data stored in different format (e. g. images and json) you can use combine_reader. combine_reader allows specify reading scheme depends on file names. It use parameter scheme for describing reading approaches as dictionary where keys are regular expressions for file names, values are reader_name.

type: combine_reader
*.json: json_reader
*.jpeg: opencv_imread

AccuracyChecker supports following list of data readers:

  • opencv_imread - read images using OpenCV library. Default color space is BGR.
    • reading_flag - (Optional) flag which specifies the way image should be read: color - default, loads color image, gray - loads image in grayscale mode, unchanged - loads image as such including alpha channel.
  • pillow_imread - read images using Pillow library. Default color space is RGB.
  • scipy_imread - read images using similar approach as in scipy.misc.imread
    Note: since 1.3.0 version the image processing module is not a part of scipy library. This reader does not use scipy anymore.
  • skimage_imread - read images using scikit-mage library. Default color space is RGB.
  • tf_imread- read images using TensorFlow. Default color space is RGB. Requires TensorFlow installation.
  • opencv_capture - read frames from video using OpenCV.
  • json_reader - read value from json file.
    • key - key for reading from stored in json dictionary.
  • annotation_features_extractor - read features from annotation.
    • features - list of features. All features should be fields of annotation representation.
  • numpy_reader - read numpy dumped files (npy or npz formats are supported for reading)
    • keys - comma-separated list of model input names
    • separator - separator symbol between input identifier and file identifier
    • id_sep - separator symbol between input name and record number in input identifier
    • block - block mode (batch - oriented). In this mode reader returns whole variable.
  • numpy_txt_reader- read data stored in text format to numpy array.
  • numpy_dict_reader - read and unpack dictionaries saved in numpy files.
  • nifti_reader - read NifTI data format
    • channels_first - allows read nifti files and transpose in order where channels first (Optional, default False)
  • wav_reader - read WAV file into NumPy array. Also gets the samplerate.
  • dicom_reader - read images stored in DICOM format.
  • pickle_reader - read data stored in pickle file. Supported formats of pickle content:
    1. numeric data array
    2. numeric data array + metadata stored in dictionary
  • rawpy - read raw image formats using rawpy library.
    • postprocess - allow image postprocessing and normalization (Optional, default True).
  • byte_reader - read raw binary data and wrap them to numpy-array.