# Copyright (C) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in project root for information.
import sys
if sys.version >= "3":
basestring = str
import pyspark
from pyspark import SparkContext
from pyspark import sql
from pyspark.ml.param.shared import *
from pyspark.sql import DataFrame
[docs]def readFromPaths(df, pathCol, imageCol="image"):
"""
Reads images from a column of filenames
Args:
df (DataFrame): The DataFrame to be processed
pathCol (str): The name of the column containing filenames
imageCol (str): The name of the added column of images
Returns:
df: The dataframe with loaded images
"""
ctx = SparkContext.getOrCreate()
jvm = ctx.getOrCreate()._jvm
reader = jvm.com.microsoft.azure.synapse.ml.io.image.ImageUtils
jresult = reader.readFromPaths(df._jdf, pathCol, imageCol)
sql_ctx = pyspark.SQLContext.getOrCreate(ctx)
return DataFrame(jresult, sql_ctx)
[docs]def readFromStrings(df, bytesCol, imageCol="image", dropPrefix=False):
"""
Reads images from a column of filenames
Args:
df (DataFrame): The DataFrame to be processed
pathCol (str): The name of the column containing filenames
imageCol (str): The name of the added column of images
Returns:
df: The dataframe with loaded images
"""
ctx = SparkContext.getOrCreate()
jvm = ctx.getOrCreate()._jvm
reader = jvm.com.microsoft.azure.synapse.ml.io.image.ImageUtils
jresult = reader.readFromStrings(df._jdf, bytesCol, imageCol, dropPrefix)
sql_ctx = pyspark.SQLContext.getOrCreate(ctx)
return DataFrame(jresult, sql_ctx)