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import pandas as pd
from pathlib import Path
audio_attacks_with_variations = [
"random_noise",
"lowpass_filter",
"highpass_filter",
"boost_audio",
"duck_audio",
"shush",
]
attacks_plot_metrics = ["bit_acc", "log10_p_value", "TPR", "FPR", "watermark_det_score"]
image_attacks_with_variations = [
"center_crop",
"jpeg",
"brightness",
"contrast",
"saturation",
"sharpness",
"resize",
"perspective",
"median_filter",
"hue",
"gaussian_blur",
]
video_attacks_with_variations = [
"Rotate",
"Resize",
"Crop",
"Brightness",
"Contrast",
"Saturation",
"H264",
"H264rgb",
"H265",
]
def plot_data(metric, selected_attack, all_attacks_df):
attack_df = all_attacks_df[all_attacks_df.attack == selected_attack]
# if metric == "None":
# return gr.LinePlot(x_bin=None)
# return gr.LinePlot(
# attack_df,
# x="strength",
# y=metric,
# color="model",
# )
def mk_variations(
all_attacks_df,
metrics: list[str] = attacks_plot_metrics,
attacks_with_variations: list[str] = audio_attacks_with_variations,
):
# all_attacks_df = pd.read_csv(csv_file)
# print(all_attacks_df)
# print(csv_file)
# with gr.Row():
# group_by = gr.Radio(metrics, value=metrics[0], label="Choose metric")
# attacks_dropdown = gr.Dropdown(
# attacks_with_variations,
# label=attacks_with_variations[0],
# info="Select attack",
# )
# attacks_by_strength = plot_data(
# group_by.value, attacks_dropdown.value, all_attacks_df
# )
# all_graphs = [
# attacks_by_strength,
# ]
# group_by.change(
# lambda group: plot_data(group, attacks_dropdown.value, all_attacks_df),
# group_by,
# all_graphs,
# )
# attacks_dropdown.change(
# lambda attack: plot_data(group_by.value, attack, all_attacks_df),
# attacks_dropdown,
# all_graphs,
# )
return {
"metrics": metrics,
"attacks_with_variations": attacks_with_variations,
"all_attacks_df": all_attacks_df.to_dict(orient="records"),
}
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