Documentation Index
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add_labels_to_dataframe
edgeimpulse.experimental.util.add_labels_to_dataframe(
df,
labels,
label_col_name='label'
)
Adds labels to a DataFrame based on provided label information.
Parameters
- df
- labels
- label_col_name=‘label’
convert_json_cbor_to_dataframe
edgeimpulse.experimental.util.convert_json_cbor_to_dataframe(
data,
ts_col_name=None
)
Converts JSON CBOR data to a pandas DataFrame.
Parameters
convert_sample_to_dataframe
edgeimpulse.experimental.util.convert_sample_to_dataframe(
sample,
label_col_name: str | None = 'label',
ts_col_name: str | None = None
)
Converts a sample to a DataFrame and adds labels if provided.
Parameters
- sample
- label_col_name: str | None = ‘label’
- ts_col_name: str | None = None
fetch_samples
edgeimpulse.experimental.util.fetch_samples(
filename: str | None = None,
category: str | None = None,
labels: str | None = None,
max_workers=None
)
Fetch samples based on the provided parameters and stream them by their IDs.
Parameters
- filename: str | None = None
- category: str | None = None
- labels: str | None = None
- max_workers=None
generate_labels_from_dataframe
edgeimpulse.experimental.util.generate_labels_from_dataframe(
df,
label_col='label',
file_name=None
)
Generates structured labels from a DataFrame based on transitions in the specified label column.
This function iterates over the rows of a pandas DataFrame and detects changes in the values of
a specified label column. It groups consecutive rows with the same label value, and for each group,
it returns a dictionary containing the start index, end index, and the label. Optionally, the result
can be returned in a dictionary format, compatible with file saving, including the file name.
Parameters
- df
- label_col=‘label’
- file_name=None