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.

reader:
  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.

reader:
  type: combine_reader
  scheme:
    *.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.