cads-sdk 0.0.27

Function to help Data Scientist work more effectively with DWH

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cads-sdk: Functions to help Data Scientist work more effectively with unstructured data and datalake

What is it?

cads-sdk Function to help Data Scientist work more effectively with unstructured data. Include different function work with image, audio

Main Features

Here are just a few of the things that cads-sdk does well:

• Data pre-processing: Faster upto 25% compare with cv2.imread()
  • Image pre-processing, convert from Image Folder/Zipfile to Parquet/delta, ready for training
  • Audio pre-processing, convert from Folder Audio, ready for training
• Optimize storage: Does not reduce image quality but optimize memory 12%
  • Decrease number of small file
  • Take advantage of compression zstd parquet
• View image/audio but not get system down (browser ran out of memory)

Install

Binary installers for the latest released version are available at the Python Package Index (PyPI).

# with PyPI
pip install cads-sdk

Dependencies

• spark-sdk - PySpark, PyArrow add on function
• opencv-python - Wrapper package for OpenCV python bindings
• petastorm - Petastorm is a library enabling the use of Parquet storage from Tensorflow, Pytorch, and other Python-based ML training frameworks
• pandas - Powerful data structures for data analysis, time series, and statistics

Installation from sources

To install cads-sdk from source you need Cython in addition to the normal dependencies above. Cython can be installed from PyPI:

pip install cython

In the cads-sdk directory (same one where you found this file after cloning the git repo), execute:

python setup.py install

Documentation

The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable

Image

Convert a folder image to parquet

from cads_sdk.nosql.converter import ConvertFromFolderImage

converter = ConvertFromFolderImage(
              input_path="/path/to/folder/**/*.jpg",
              input_type = 'jpg' # 'jpg' | ('jpg', 'png')
              input_recursive = True,

              #setting output
              output_path = f"file:/output/path/image_storage",

              # setting converter
              image_type = 'jpg',
              image_color = 3,
              resize_mode=None, # |padding|resize
              size = [(212,212),
                     (597, 597)],
             )

converter.execute()

# convert directly from .zip file to parquet
from cads_sdk.nosql.converter import ConvertFromZipImage

converter = ConvertFromZipImage(
              input_path="/path/to/image_storage/ETHZ.zip",
              input_recursive = True, # will loop through folder to get all pattern
              input_type = 'jpg' # 'jpg' | ('jpg', 'png')

              #setting output
              output_path = f"file:/output/path/img_ethz.parquet",
              table_name = 'img_ethz',
              database = 'default',
              file_format = 'parquet', # delta|parquet
              compression = 'zstd', # zstd|snappy
              # setting converter
              image_type = 'png',
              image_color = 3,
              resize_mode=None, # |padding|resize
              size = [(1080,1920)],
              debug = False
             )

converter.execute()

Convert a Image parquet file back to Image Folder

from cads_sdk.nosql.converter import ConvertToFolderImage

converter = ConvertToFolderImage(
    input_path = '/user/username/image/img_user_device_jpg_212_212.parquet',
    raw_input_path = "/home/username/image_storage/device_images/**/*.jpg",
    output_path = './abc/'
)

converter.execute()

Function to read image

from cads.nosql import display
import cads_sdk as ss

df = ss.sql("""select * from parquet.`/user/duyvnc/image/img_images_jpg_212_212.parquet`""")
pdf = df.toPandasImage(limit=50)
pdf

pdf = ss.sql("""
select *
from parquet.`file:/home/duyvnc/image_storage/img_mot17_1080_1920.parquet`
limit 100
""").toPandasImage(mode='BGR')

Pytorch API

from cads_sdk.nosql import codec
from petastorm import make_reader, TransformSpec
from petastorm.pytorch import DataLoader
num_epochs = 10
with DataLoader(reader=make_reader('{}/train'.format(dataset_url), reader_pool_type='dummy', num_epochs=num_epochs,
                            transform_spec=transform), batch_size=32) as train_loader:
    train(model, device, train_loader, 2000, optimizer, num_epochs)

Audio

Suport pcm, mp3, wav format

Convert a folder audio to parquet

from cads_sdk.nosql.converter import ConvertFromFolderImage

converter = ConvertFromFolderAudio(
              input_path='/path/to/audio_wav/*.wav', #(1)
              input_type = 'wav' # 'wav'| 'mp3' | 'pcm'
              input_recursive = False,
              output_path = f"file:/output/path/audio_wav.parquet",
             )

converter.execute()

Convert a parquet to folder audio

converter = ConvertToFolderAudio(
input_path = 'file:/path/to/audio_wav.parquet',
raw_input_path = '/path/to/audio_wav/*.wav', #(1) # auto replace '/path/to/audio_wav/' to ''
output_path = './output/path',
write_mode = "recovery"
)

converter.execute()

Listen audio in parquet

from cads_sdk.nosql.display import Audio
Audio('file:/path/to/audio_mp3.parquet')

Video

from cads_sdk.nosql.converter import ConvertFromVideo2Image
converter = ConvertFromVideo2Image(
              input_path='/home/username/vid/palawan1.mp4',
              output_path = f"file:/home/username/vid_image.parquet",
             )

converter.execute()

For more information use class instance

ConvertFromFolderImage.__doc__