Read image files within a directory and convert each file into a record within the resulting Spark dataframe. The output will be a Spark dataframe consisting of struct types containing the following attributes:
origin: StringType
height: IntegerType
width: IntegerType
nChannels: IntegerType
mode: IntegerType
data: BinaryType
Usage
spark_read_image(
sc,
name = NULL,
dir = name,
drop_invalid = TRUE,
repartition = 0,
memory = TRUE,
overwrite = TRUE
)Arguments
- sc
A
spark_connection.- name
The name to assign to the newly generated table.
- dir
Directory to read binary files from.
- drop_invalid
Whether to drop files that are not valid images from the result (default: TRUE).
- repartition
The number of partitions used to distribute the generated table. Use 0 (the default) to avoid partitioning.
- memory
Boolean; should the data be loaded eagerly into memory? (That is, should the table be cached?)
- overwrite
Boolean; overwrite the table with the given name if it already exists?
See also
Other Spark serialization routines:
collect_from_rds(),
spark_insert_table(),
spark_load_table(),
spark_read(),
spark_read_avro(),
spark_read_binary(),
spark_read_csv(),
spark_read_delta(),
spark_read_jdbc(),
spark_read_json(),
spark_read_libsvm(),
spark_read_orc(),
spark_read_parquet(),
spark_read_source(),
spark_read_table(),
spark_read_text(),
spark_save_table(),
spark_write_avro(),
spark_write_csv(),
spark_write_delta(),
spark_write_jdbc(),
spark_write_json(),
spark_write_orc(),
spark_write_parquet(),
spark_write_source(),
spark_write_table(),
spark_write_text()