app.py

import os
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import seaborn as sns
from facenet_pytorch import InceptionResnetV1, MTCNN
import mediapipe as mp
from fer import FER
from scipy import interpolate
from sklearn.cluster import DBSCAN, KMeans
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.metrics import silhouette_score
import umap
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from moviepy.editor import VideoFileClip
from PIL import Image
import gradio as gr
import tempfile
import shutil

# Suppress TensorFlow warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf

tf.get_logger().setLevel('ERROR')

matplotlib.rcParams['figure.dpi'] = 400
matplotlib.rcParams['savefig.dpi'] = 400

# Initialize models and other global variables
device = 'cuda' if torch.cuda.is_available() else 'cpu'

mtcnn = MTCNN(keep_all=False, device=device, thresholds=[0.999, 0.999, 0.999], min_face_size=100,
              selection_method='largest')
model = InceptionResnetV1(pretrained='vggface2').eval().to(device)
mp_face_mesh = mp.solutions.face_mesh
face_mesh = mp_face_mesh.FaceMesh(static_image_mode=False, max_num_faces=1, min_detection_confidence=0.5)
emotion_detector = FER(mtcnn=False)

def frame_to_timecode(frame_num, total_frames, duration):
    total_seconds = (frame_num / total_frames) * duration
    hours = int(total_seconds // 3600)
    minutes = int((total_seconds % 3600) // 60)
    seconds = int(total_seconds % 60)
    milliseconds = int((total_seconds - int(total_seconds)) * 1000)
    return f"{hours:02d}:{minutes:02d}:{seconds:02d}.{milliseconds:03d}"

def get_face_embedding_and_emotion(face_img):
    face_tensor = torch.tensor(face_img).permute(2, 0, 1).unsqueeze(0).float() / 255
    face_tensor = (face_tensor - 0.5) / 0.5
    face_tensor = face_tensor.to(device)
    with torch.no_grad():
        embedding = model(face_tensor)

    emotions = emotion_detector.detect_emotions(face_img)
    if emotions:
        emotion_dict = emotions[0]['emotions']
    else:
        emotion_dict = {e: 0 for e in ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']}

    return embedding.cpu().numpy().flatten(), emotion_dict

def alignFace(img):
    img_raw = img.copy()
    results = face_mesh.process(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    if not results.multi_face_landmarks:
        return None
    landmarks = results.multi_face_landmarks[0].landmark
    left_eye = np.array([[landmarks[33].x, landmarks[33].y], [landmarks[160].x, landmarks[160].y],
                         [landmarks[158].x, landmarks[158].y], [landmarks[144].x, landmarks[144].y],
                         [landmarks[153].x, landmarks[153].y], [landmarks[145].x, landmarks[145].y]])
    right_eye = np.array([[landmarks[362].x, landmarks[362].y], [landmarks[385].x, landmarks[385].y],
                          [landmarks[387].x, landmarks[387].y], [landmarks[263].x, landmarks[263].y],
                          [landmarks[373].x, landmarks[373].y], [landmarks[380].x, landmarks[380].y]])
    left_eye_center = left_eye.mean(axis=0).astype(np.int32)
    right_eye_center = right_eye.mean(axis=0).astype(np.int32)
    dY = right_eye_center[1] - left_eye_center[1]
    dX = right_eye_center[0] - left_eye_center[0]
    angle = np.degrees(np.arctan2(dY, dX))
    desired_angle = 0
    angle_diff = desired_angle - angle
    height, width = img_raw.shape[:2]
    center = (width // 2, height // 2)
    rotation_matrix = cv2.getRotationMatrix2D(center, angle_diff, 1)
    new_img = cv2.warpAffine(img_raw, rotation_matrix, (width, height))
    return new_img

def extract_frames(video_path, output_folder, desired_fps, progress_callback=None):
    os.makedirs(output_folder, exist_ok=True)
    clip = VideoFileClip(video_path)
    original_fps = clip.fps
    duration = clip.duration
    total_frames = int(duration * original_fps)
    step = max(1, original_fps / desired_fps)
    total_frames_to_extract = int(total_frames / step)

    frame_count = 0
    for t in np.arange(0, duration, step / original_fps):
        frame = clip.get_frame(t)
        img = Image.fromarray(frame)
        img.save(os.path.join(output_folder, f"frame_{frame_count:04d}.jpg"))
        frame_count += 1
        if progress_callback:
            progress = min(100, (frame_count / total_frames_to_extract) * 100)
            progress_callback(progress, f"Extracting frame")
        if frame_count >= total_frames_to_extract:
            break
    clip.close()
    return frame_count, original_fps

def process_frames(frames_folder, aligned_faces_folder, frame_count, progress, batch_size):
    embeddings_by_frame = {}
    emotions_by_frame = {}
    frame_files = sorted([f for f in os.listdir(frames_folder) if f.endswith('.jpg')])

    for i in range(0, len(frame_files), batch_size):
        batch_files = frame_files[i:i + batch_size]
        batch_frames = []
        batch_nums = []

        for frame_file in batch_files:
            frame_num = int(frame_file.split('_')[1].split('.')[0])
            frame_path = os.path.join(frames_folder, frame_file)
            frame = cv2.imread(frame_path)
            if frame is not None:
                batch_frames.append(frame)
                batch_nums.append(frame_num)

        if batch_frames:
            batch_boxes, batch_probs = mtcnn.detect(batch_frames)

            for j, (frame, frame_num, boxes, probs) in enumerate(
                    zip(batch_frames, batch_nums, batch_boxes, batch_probs)):
                if boxes is not None and len(boxes) > 0 and probs[0] >= 0.99:
                    x1, y1, x2, y2 = [int(b) for b in boxes[0]]
                    face = frame[y1:y2, x1:x2]
                    if face.size > 0:
                        aligned_face = alignFace(face)
                        if aligned_face is not None:
                            aligned_face_resized = cv2.resize(aligned_face, (160, 160))
                            output_path = os.path.join(aligned_faces_folder, f"frame_{frame_num}_face.jpg")
                            cv2.imwrite(output_path, aligned_face_resized)
                            embedding, emotion = get_face_embedding_and_emotion(aligned_face_resized)
                            embeddings_by_frame[frame_num] = embedding
                            emotions_by_frame[frame_num] = emotion

        progress((i + len(batch_files)) / frame_count,
                 f"Processing frames {i + 1} to {min(i + len(batch_files), frame_count)} of {frame_count}")

    return embeddings_by_frame, emotions_by_frame


def cluster_embeddings(embeddings):
    if len(embeddings) < 2:
        print("Not enough embeddings for clustering. Assigning all to one cluster.")
        return np.zeros(len(embeddings), dtype=int)

    scaler = StandardScaler()
    embeddings_scaled = scaler.fit_transform(embeddings)

    # Use DBSCAN for adaptive clustering
    dbscan = DBSCAN(eps=0.5, min_samples=5)  # Adjust these parameters as needed
    clusters = dbscan.fit_predict(embeddings_scaled)

    # If DBSCAN couldn't find meaningful clusters, fall back to KMeans
    if len(set(clusters)) == 1:
        best_n_clusters = 1
        best_score = -1
        for n_clusters in range(2, min(5, len(embeddings))):
            kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
            labels = kmeans.fit_predict(embeddings_scaled)
            score = silhouette_score(embeddings_scaled, labels)
            if score > best_score:
                best_score = score
                best_n_clusters = n_clusters

        kmeans = KMeans(n_clusters=best_n_clusters, random_state=42, n_init=10)
        clusters = kmeans.fit_predict(embeddings_scaled)

    return clusters

def organize_faces_by_person(embeddings_by_frame, clusters, aligned_faces_folder, organized_faces_folder):
    for (frame_num, embedding), cluster in zip(embeddings_by_frame.items(), clusters):
        person_folder = os.path.join(organized_faces_folder, f"person_{cluster}")
        os.makedirs(person_folder, exist_ok=True)
        src = os.path.join(aligned_faces_folder, f"frame_{frame_num}_face.jpg")
        dst = os.path.join(person_folder, f"frame_{frame_num}_face.jpg")
        shutil.copy(src, dst)

def save_person_data_to_csv(embeddings_by_frame, emotions_by_frame, clusters, desired_fps, original_fps, output_folder,
                            num_components, video_duration):
    emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
    person_data = {}

    for (frame_num, embedding), (_, emotion_dict), cluster in zip(embeddings_by_frame.items(),
                                                                  emotions_by_frame.items(), clusters):
        if cluster not in person_data:
            person_data[cluster] = []
        person_data[cluster].append((frame_num, embedding, {e: emotion_dict[e] for e in emotions}))

    largest_cluster = max(person_data, key=lambda k: len(person_data[k]))

    data = person_data[largest_cluster]
    data.sort(key=lambda x: x[0])
    frames, embeddings, emotions_data = zip(*data)

    embeddings_array = np.array(embeddings)
    np.save(os.path.join(output_folder, 'face_embeddings.npy'), embeddings_array)

    reducer = umap.UMAP(n_components=num_components, random_state=1)
    embeddings_reduced = reducer.fit_transform(embeddings)

    scaler = MinMaxScaler(feature_range=(0, 1))
    embeddings_reduced_normalized = scaler.fit_transform(embeddings_reduced)

    total_frames = max(frames)
    timecodes = [frame_to_timecode(frame, total_frames, video_duration) for frame in frames]
    times_in_minutes = [frame / total_frames * video_duration / 60 for frame in frames]

    df_data = {
        'Frame': frames,
        'Timecode': timecodes,
        'Time (Minutes)': times_in_minutes,
        'Embedding_Index': range(len(embeddings))
    }

    for i in range(num_components):
        df_data[f'Comp {i + 1}'] = embeddings_reduced_normalized[:, i]

    for emotion in emotions:
        df_data[emotion] = [e[emotion] for e in emotions_data]

    df = pd.DataFrame(df_data)

    return df, largest_cluster

class LSTMAutoencoder(nn.Module):
    def __init__(self, input_size, hidden_size=64, num_layers=2):
        super(LSTMAutoencoder, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, input_size)

    def forward(self, x):
        outputs, (hidden, _) = self.lstm(x)
        out = self.fc(outputs)
        return out

def lstm_anomaly_detection(X, feature_columns, num_anomalies=10, epochs=100, batch_size=64):
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    X = torch.FloatTensor(X).to(device)
    if X.dim() == 2:
        X = X.unsqueeze(0)
    elif X.dim() == 1:
        X = X.unsqueeze(0).unsqueeze(2)
    elif X.dim() > 3:
        raise ValueError(f"Input X should be 1D, 2D or 3D, but got {X.dim()} dimensions")

    print(f"X shape after reshaping: {X.shape}")

    train_size = int(0.85 * X.shape[1])
    X_train, X_val = X[:, :train_size, :], X[:, train_size:, :]

    model = LSTMAutoencoder(input_size=X.shape[2]).to(device)
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters())

    for epoch in range(epochs):
        model.train()
        optimizer.zero_grad()
        output_train = model(X_train)
        loss_train = criterion(output_train, X_train.squeeze(0))
        loss_train.backward()
        optimizer.step()

        model.eval()
        with torch.no_grad():
            output_val = model(X_val)
            loss_val = criterion(output_val, X_val.squeeze(0))

    model.eval()
    with torch.no_grad():
        reconstructed = model(X).squeeze(0).cpu().numpy()

    mse_all = np.mean(np.power(X.squeeze(0).cpu().numpy() - reconstructed, 2), axis=1)
    top_indices_all = mse_all.argsort()[-num_anomalies:][::-1]
    anomalies_all = np.zeros(len(mse_all), dtype=bool)
    anomalies_all[top_indices_all] = True

    component_columns = [col for col in feature_columns if col.startswith('Comp')]
    component_indices = [feature_columns.index(col) for col in component_columns]

    if len(component_indices) > 0:
        mse_comp = np.mean(
            np.power(X.squeeze(0).cpu().numpy()[:, component_indices] - reconstructed[:, component_indices], 2), axis=1)
    else:
        mse_comp = mse_all

    top_indices_comp = mse_comp.argsort()[-num_anomalies:][::-1]
    anomalies_comp = np.zeros(len(mse_comp), dtype=bool)
    anomalies_comp[top_indices_comp] = True

    return (anomalies_all, mse_all, top_indices_all,
            anomalies_comp, mse_comp, top_indices_comp,
            model)

from scipy import interpolate

def plot_with_segments(ax, df_filtered, y_column, color):
    segments = []
    current_segment = []
    for i, (time, score) in enumerate(zip(df_filtered['Seconds'], df_filtered[y_column])):
        if i > 0 and time - df_filtered['Seconds'].iloc[i-1] > 1:  # Gap of more than 1 second
            if current_segment:
                segments.append(current_segment)
                current_segment = []
        current_segment.append((time, score))
    if current_segment:
        segments.append(current_segment)

    for segment in segments:
        times, scores = zip(*segment)
        if len(times) > 3:
            try:
                # Use scipy's interpolate to create a smooth curve
                f = interpolate.interp1d(times, scores, kind='cubic')
                smooth_times = np.linspace(min(times), max(times), num=200)
                smooth_scores = f(smooth_times)
                ax.plot(smooth_times, smooth_scores, color=color, linewidth=1.5)
            except ValueError:
                # Fall back to linear interpolation if cubic fails
                f = interpolate.interp1d(times, scores, kind='linear')
                smooth_times = np.linspace(min(times), max(times), num=200)
                smooth_scores = f(smooth_times)
                ax.plot(smooth_times, smooth_scores, color=color, linewidth=1.5)
        else:
            # For very short segments, just plot the points
            ax.plot(times, scores, color=color, linewidth=1.5)

def plot_anomaly_scores(df, anomaly_scores, top_indices, title):
    plt.figure(figsize=(16, 8), dpi=400)
    fig, ax = plt.subplots(figsize=(16, 8))

    df['Seconds'] = df['Timecode'].apply(
        lambda x: sum(float(t) * 60 ** i for i, t in enumerate(reversed(x.split(':')))))

    # Filter out rows with no data
    mask = ~np.isnan(anomaly_scores)
    df_filtered = df[mask].copy()
    df_filtered['anomaly_scores'] = anomaly_scores[mask]

    if df_filtered.empty:
        ax.text(0.5, 0.5, "No data available", ha='center', va='center')
    else:
        plot_with_segments(ax, df_filtered, 'anomaly_scores', 'blue')

        # Highlight top anomalies
        top_indices_filtered = [i for i in top_indices if i in df_filtered.index]
        ax.scatter(df_filtered['Seconds'].iloc[top_indices_filtered],
                   df_filtered['anomaly_scores'].iloc[top_indices_filtered],
                   color='red', s=100, zorder=5)

    max_seconds = df['Seconds'].max()  # Use the full range for x-axis
    ax.set_xlim(0, max_seconds)
    num_ticks = 80
    ax.set_xticks(np.linspace(0, max_seconds, num_ticks))
    ax.set_xticklabels([f"{int(x // 60):02d}:{int(x % 60):02d}" for x in ax.get_xticks()],
                       rotation=90, ha='center', va='top')

    ax.set_xlabel('Time')
    ax.set_ylabel('Anomaly Score')
    ax.set_title(f'Anomaly Scores Over Time ({title})')

    ax.grid(True, linestyle='--', alpha=0.7)
    plt.tight_layout()
    return fig

def plot_emotion(df, emotion, num_anomalies, color):
    plt.figure(figsize=(16, 8), dpi=400)
    fig, ax = plt.subplots(figsize=(16, 8))

    df['Seconds'] = df['Timecode'].apply(
        lambda x: sum(float(t) * 60 ** i for i, t in enumerate(reversed(x.split(':')))))

    # Filter out rows with no data
    mask = ~np.isnan(df[emotion])
    df_filtered = df[mask]

    if df_filtered.empty:
        ax.text(0.5, 0.5, "No data available", ha='center', va='center')
    else:
        plot_with_segments(ax, df_filtered, emotion, color)

        # Highlight top anomalies
        top_indices = np.argsort(df_filtered[emotion].values)[-num_anomalies:][::-1]
        ax.scatter(df_filtered['Seconds'].iloc[top_indices],
                   df_filtered[emotion].iloc[top_indices],
                   color='red', s=100, zorder=5)

    max_seconds = df['Seconds'].max()  # Use the full range for x-axis
    ax.set_xlim(0, max_seconds)
    num_ticks = 80
    ax.set_xticks(np.linspace(0, max_seconds, num_ticks))
    ax.set_xticklabels([f"{int(x // 60):02d}:{int(x % 60):02d}" for x in ax.get_xticks()],
                       rotation=90, ha='center', va='top')

    ax.set_xlabel('Time')
    ax.set_ylabel(f'{emotion.capitalize()} Score')
    ax.set_title(f'{emotion.capitalize()} Scores Over Time (Top {num_anomalies} in Red)')

    ax.grid(True, linestyle='--', alpha=0.7)
    plt.tight_layout()
    return fig

def get_random_face_samples(organized_faces_folder, output_folder):
    face_samples = {}
    for cluster_folder in os.listdir(organized_faces_folder):
        if cluster_folder.startswith("person_"):
            cluster_id = int(cluster_folder.split("_")[1])
            person_folder = os.path.join(organized_faces_folder, cluster_folder)
            face_files = [f for f in os.listdir(person_folder) if f.endswith('.jpg')]
            if face_files:
                random_face = np.random.choice(face_files)
                face_path = os.path.join(person_folder, random_face)
                output_path = os.path.join(output_folder, f"face_sample_person_{cluster_id}.jpg")
                face_img = cv2.imread(face_path)
                small_face = cv2.resize(face_img, (160, 160))
                cv2.imwrite(output_path, small_face)
                face_samples[cluster_id] = output_path
    return face_samples

def process_video(video_path, num_anomalies, num_components, desired_fps, batch_size, progress=gr.Progress()):
    output_folder = "output"
    os.makedirs(output_folder, exist_ok=True)

    with tempfile.TemporaryDirectory() as temp_dir:
        aligned_faces_folder = os.path.join(temp_dir, 'aligned_faces')
        organized_faces_folder = os.path.join(temp_dir, 'organized_faces')
        os.makedirs(aligned_faces_folder, exist_ok=True)
        os.makedirs(organized_faces_folder, exist_ok=True)

        clip = VideoFileClip(video_path)
        video_duration = clip.duration
        clip.close()

        progress(0, "Starting frame extraction")
        frames_folder = os.path.join(temp_dir, 'extracted_frames')

        def extraction_progress(percent, message):
            progress(percent / 100, f"Extracting frames")

        frame_count, original_fps = extract_frames(video_path, frames_folder, desired_fps, extraction_progress)

        progress(1, "Frame extraction complete")
        progress(0.3, "Processing frames")
        embeddings_by_frame, emotions_by_frame = process_frames(frames_folder, aligned_faces_folder, frame_count,
                                                                progress, batch_size)

        if not embeddings_by_frame:
            return ("No faces were extracted from the video.",
                    None, None, None, None, None, None, None, None, None)

        progress(0.6, "Clustering embeddings")
        embeddings = list(embeddings_by_frame.values())
        clusters = cluster_embeddings(embeddings)
        num_clusters = len(set(clusters))  # Get the number of unique clusters

        progress(0.7, "Organizing faces")
        organize_faces_by_person(embeddings_by_frame, clusters, aligned_faces_folder, organized_faces_folder)

        progress(0.75, "Getting face samples")
        face_samples = get_random_face_samples(organized_faces_folder, output_folder)

        progress(0.8, "Saving person data")
        df, largest_cluster = save_person_data_to_csv(embeddings_by_frame, emotions_by_frame, clusters, desired_fps,
                                                      original_fps, temp_dir, num_components, video_duration)

        progress(0.9, "Performing anomaly detection")
        feature_columns = [col for col in df.columns if
                           col not in ['Frame', 'Timecode', 'Time (Minutes)', 'Embedding_Index']]
        X = df[feature_columns].values
        print(f"Shape of input data: {X.shape}")
        print(f"Feature columns: {feature_columns}")
        try:
            anomalies_all, anomaly_scores_all, top_indices_all, anomalies_comp, anomaly_scores_comp, top_indices_comp, _ = lstm_anomaly_detection(
                X, feature_columns, num_anomalies=num_anomalies, batch_size=batch_size)
        except Exception as e:
            print(f"Error details: {str(e)}")
            return f"Error in anomaly detection: {str(e)}", None, None, None, None, None, None, None, None, None

        progress(0.95, "Generating plots")
        try:
            anomaly_plot_all = plot_anomaly_scores(df, anomaly_scores_all, top_indices_all, "All Features")
            anomaly_plot_comp = plot_anomaly_scores(df, anomaly_scores_comp, top_indices_comp, "Components Only")
            emotion_plots = [
                plot_emotion(df, 'fear', num_anomalies, 'purple'),
                plot_emotion(df, 'sad', num_anomalies, 'green'),
                plot_emotion(df, 'angry', num_anomalies, 'orange'),
                plot_emotion(df, 'happy', num_anomalies, 'darkblue'),
                plot_emotion(df, 'surprise', num_anomalies, 'gold'),
                plot_emotion(df, 'neutral', num_anomalies, 'grey')
            ]
        except Exception as e:
            return f"Error generating plots: {str(e)}", None, None, None, None, None, None, None, None, None

        progress(1.0, "Preparing results")
        results = f"Number of persons detected: {num_clusters}\n\n"
        for cluster_id in range(num_clusters):
            results += f"Person {cluster_id + 1}: {len([c for c in clusters if c == cluster_id])} frames\n"
        results += f"\nTop {num_anomalies} anomalies (All Features):\n"
        results += "\n".join([f"{score:.4f} at {timecode}" for score, timecode in
                              zip(anomaly_scores_all[top_indices_all], df['Timecode'].iloc[top_indices_all].values)])
        results += f"\n\nTop {num_anomalies} anomalies (Components Only):\n"
        results += "\n".join([f"{score:.4f} at {timecode}" for score, timecode in
                              zip(anomaly_scores_comp[top_indices_comp], df['Timecode'].iloc[top_indices_comp].values)])

        for emotion in ['fear', 'sad', 'angry', 'happy', 'surprise', 'neutral']:
            top_indices = np.argsort(df[emotion].values)[-num_anomalies:][::-1]
            results += f"\n\nTop {num_anomalies} {emotion.capitalize()} Scores:\n"
            results += "\n".join([f"{df[emotion].iloc[i]:.4f} at {df['Timecode'].iloc[i]}" for i in top_indices])

        return (
            results,
            anomaly_plot_all,
            anomaly_plot_comp,
            *emotion_plots,
            *[face_samples.get(i, None) for i in range(num_clusters)]
        )


iface = gr.Interface(
    fn=process_video,
    inputs=[
        gr.Video(),
        gr.Slider(minimum=1, maximum=20, step=1, value=10, label="Number of Anomalies"),
        gr.Slider(minimum=1, maximum=20, step=1, value=10, label="Number of Components"),
        gr.Slider(minimum=1, maximum=20, step=1, value=15, label="Desired FPS"),
        gr.Slider(minimum=1, maximum=32, step=4, value=8, label="Batch Size")
    ],
    outputs=[
        gr.Textbox(label="Anomaly Detection Results"),
        gr.Plot(label="Anomaly Scores (Facial Features + Emotions)"),
        gr.Plot(label="Anomaly Scores (Facial Features)"),
        gr.Plot(label="Fear Anomalies"),
        gr.Plot(label="Sad Anomalies"),
        gr.Plot(label="Angry Anomalies"),
        gr.Plot(label="Happy Anomalies"),
        gr.Plot(label="Surprise Anomalies"),
        gr.Plot(label="Neutral Anomalies"),
        gr.Gallery(label="Detected Persons", columns=[2], rows=[1], height="auto")
    ],
    title="Facial Expressions Anomaly Detection",
    description="""
    This application detects anomalies in facial expressions and emotions from a video input. 
    It identifies distinct persons in the video and provides a sample face for each.

    Adjust the parameters as needed:
    - Number of Anomalies: How many top anomalies or high intensities to highlight
    - Number of Components: Complexity of the facial expression model
    - Desired FPS: Frames per second to analyze (lower for faster processing)
    - Batch Size: Affects processing speed and memory usage
    """,
    allow_flagging="never"
)

if __name__ == "__main__":
    iface.launch()