Read image data into a Spark DataFrame.
R/data_interface.R
spark_read_image
Description
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
| Arguments | Description |
|---|---|
| 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_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_read(), 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()