src/examples.py

import gradio as gr

import os
import glob
import json
import re
from pathlib import Path

import json


def group_images_by_index(image_paths, is_audio=False):
    # Regular expression pattern to extract the key from each image path
    if is_audio:
        pattern = r"audio_(\d+).png"
    else:
        pattern = r"img_(\d+).png"
    # Dictionary to store the grouped images
    grouped_images = {}
    # Iterate over each image path
    for image_path in image_paths:
        # Extract the key using the regular expression pattern
        match = re.search(pattern, image_path)
        if match:
            key = int(match.group(1))

            # Add the image path to the corresponding group in the dictionary
            if key not in grouped_images:
                grouped_images[key] = []
            grouped_images[key].append(image_path)
    # Sort the dictionary by keys
    sorted_grouped_images = dict(sorted(grouped_images.items()))
    return sorted_grouped_images


def build_image_description(i, data_none, data_attack):
    if i == 0:
        fake_det_score = float(data_none["fake_det_score"])

        return f"det_score: {fake_det_score:.2f}"
    elif i == 1:
        psnr = float(data_none["psnr"])
        ssim = float(data_none["ssim"])
        lpips = float(data_none["lpips"])

        det_score = float(data_none["watermark_det_score"])
        p_value = data_none["p_value"]
        bit_acc = data_none["bit_acc"]

        return f"psnr: {psnr:.2f} ssim: {ssim:.2f} lpips: {lpips:.2f} det_score: {det_score:.2f} p_value: {p_value} bit_acc: {bit_acc}"
    elif i == 2:
        fake_det_score = float(data_attack["fake_det_score"])

        return f"det_score: {fake_det_score:.2f}"
    elif i == 3:
        det_score = float(data_attack["watermark_det_score"])
        p_value = data_attack["p_value"]
        word_acc = data_attack["word_acc"]
        bit_acc = data_attack["bit_acc"]

        return f"word_acc: {word_acc:.2f} det_score: {det_score:.2f} p_value: {p_value} bit_acc: {bit_acc}"


def build_audio_description(i, data_none, data_attack):
    if i == 0:
        tn_detect_prob = float(data_none["tn_detect_prob"])

        return f"det_score: {tn_detect_prob:.2f}"
    elif i == 1:
        snr = float(data_none["snr"])
        sisnr = float(data_none["sisnr"])
        stoi = float(data_none["stoi"])
        pesq = float(data_none["pesq"])

        det_score = float(data_none["detect_prob"])
        bit_acc = data_none["ba"]

        return f"snr: {snr:.2f} sisnr: {sisnr:.2f} stoi: {stoi:.2f} pesq: {pesq:.2f} det_score: {det_score:.2f} bit_acc: {bit_acc}"
    elif i == 2:
        tn_detect_prob = float(data_attack["tn_detect_prob"])

        return f"det_score: {tn_detect_prob:.2f}"
    elif i == 3:
        det_score = float(data_attack["detect_prob"])
        bit_acc = data_attack["ba"]

        return f"det_score: {det_score:.2f} bit_acc: {bit_acc}"


def build_image_infos(abs_path: Path):

    with (abs_path / "data/image_eval_results.json").open("r") as f:
        image_data = json.loads(f.read())

    examples_dir = Path("./examples/image")
    image_infos = {}

    model_image_infos = {}
    for model_name in os.listdir(examples_dir):
        model_attacks_dir = examples_dir / model_name

        for attack_name in os.listdir(model_attacks_dir):
            attack_dir = model_attacks_dir / attack_name
            image_paths = glob.glob(f"{attack_dir}/*.png")

            all_files = []

            for i, files in group_images_by_index(image_paths).items():
                data_none = image_data["eval"]["val2014"]["wam"]["none"][i]
                data_attack = image_data["eval"]["val2014"]["wam"][attack_name][i]

                files = sorted([(f, Path(f).stem) for f in files], key=lambda x: x[1])
                files = files[2:] + files[:2]

                files = [
                    (f, f"{n}\n{build_image_description(i, data_none, data_attack)}")
                    for i, (f, n) in enumerate(files)
                ]

                all_files.extend(files)

            model_image_infos[attack_name] = all_files

        image_infos[model_name] = model_image_infos

    return image_infos


def build_audio_infos(abs_path: Path):

    with (abs_path / "data/audio_eval_results.json").open("r") as f:
        audio_data = json.loads(f.read())

    examples_dir = Path("./examples/audio")
    audio_infos = {}

    model_audio_infos = {}
    for model_name in os.listdir(examples_dir):
        model_attacks_dir = examples_dir / model_name

        for attack_name in os.listdir(model_attacks_dir):
            attack_dir = model_attacks_dir / attack_name
            image_paths = glob.glob(f"{attack_dir}/*.png")

            all_files = []

            for i, files in group_images_by_index(image_paths, is_audio=True).items():
                data_none = audio_data["eval"]["ravdess"][model_name]["identity"][i]
                data_attack = audio_data["eval"]["ravdess"][model_name][attack_name][i]

                files = sorted([(f, Path(f).stem) for f in files], key=lambda x: x[1])
                files = files[2:] + files[:2]

                files = [
                    (f, f"{n}\n{build_audio_description(i, data_none, data_attack)}")
                    for i, (f, n) in enumerate(files)
                ]

                all_files.extend(files)

            model_audio_infos[attack_name] = all_files

        audio_infos[model_name] = model_audio_infos

    return audio_infos


def examples_tab(abs_path: Path):
    image_infos = build_image_infos(abs_path)

    # First combo box (category selection)
    model_choice = gr.Dropdown(
        choices=list(image_infos.keys()),
        label="Select a Model",
        value=None,
    )
    # Second combo box (subcategory selection)
    # Initialize with options from the first category by default
    attack_choice = gr.Dropdown(
        choices=list(image_infos["wam"].keys()),
        label="Select an Attack",
        value=None,
    )

    # Gallery component to display images
    gallery = gr.Gallery(
        label="Image Gallery",
        columns=4,
        rows=1,
    )

    # Update options for the second combo box when the first one changes
    def update_subcategories(selected_category):
        values = list(image_infos[selected_category].keys())
        values = [(v, v) for v in values]
        attack_choice.choices = values
        # return gr.Dropdown.update(choices=list(image_infos[selected_category].keys()))

    # Function to load images based on selections from both combo boxes
    def load_images(category, subcategory):
        return image_infos.get(category, {}).get(subcategory, [])

    # Update gallery based on both combo box selections
    model_choice.change(
        fn=update_subcategories, inputs=model_choice, outputs=attack_choice
    )
    attack_choice.change(
        fn=load_images, inputs=[model_choice, attack_choice], outputs=gallery
    )


def audio_examples_tab(abs_path: Path):
    audio_infos = build_audio_infos(abs_path)

    # First combo box (category selection)
    model_choice = gr.Dropdown(
        choices=list(audio_infos.keys()),
        label="Select a Model",
        value=None,
    )
    # Second combo box (subcategory selection)
    # Initialize with options from the first category by default
    attack_choice = gr.Dropdown(
        choices=list(audio_infos["audioseal"].keys()),
        label="Select an Attack",
        value=None,
    )

    # Gallery component to display images
    gallery = gr.Gallery(
        label="Image Gallery", columns=4, rows=1, object_fit="scale-down"
    )

    # Update options for the second combo box when the first one changes
    def update_subcategories(selected_category):
        values = list(audio_infos[selected_category].keys())
        values = [(v, v) for v in values]
        attack_choice.choices = values
        # return gr.Dropdown.update(choices=list(image_infos[selected_category].keys()))

    # Function to load images based on selections from both combo boxes
    def load_audios(category, subcategory):
        return audio_infos.get(category, {}).get(subcategory, [])

    # Update gallery based on both combo box selections
    model_choice.change(
        fn=update_subcategories, inputs=model_choice, outputs=attack_choice
    )
    attack_choice.change(
        fn=load_audios, inputs=[model_choice, attack_choice], outputs=gallery
    )