visualization.py

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
import matplotlib.colors as mcolors
from matplotlib.colors import LinearSegmentedColormap
import seaborn as sns
import numpy as np
import pandas as pd
import cv2
from moviepy.editor import VideoFileClip, AudioFileClip, CompositeVideoClip, ImageClip, VideoClip, concatenate_videoclips
from moviepy.video.fx.all import resize
from PIL import Image, ImageDraw, ImageFont
from matplotlib.patches import Rectangle
from utils import seconds_to_timecode
from anomaly_detection import determine_anomalies
from scipy import interpolate
import gradio as gr
import os

def plot_mse(df, mse_values, title, color='navy', time_threshold=3, anomaly_threshold=4):
    plt.figure(figsize=(16, 8), dpi=300)
    fig, ax = plt.subplots(figsize=(16, 8))

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

    # Ensure df and mse_values have the same length and remove NaN values
    min_length = min(len(df), len(mse_values))
    df = df.iloc[:min_length].copy()
    mse_values = mse_values[:min_length]

    # Remove NaN values and create a mask for valid data
    valid_mask = ~np.isnan(mse_values)
    df = df[valid_mask]
    mse_values = mse_values[valid_mask]

    # Function to identify continuous segments
    def get_continuous_segments(seconds, values, max_gap=1):
        segments = []
        current_segment = []
        for i, (sec, val) in enumerate(zip(seconds, values)):
            if not current_segment or (sec - current_segment[-1][0] <= max_gap):
                current_segment.append((sec, val))
            else:
                segments.append(current_segment)
                current_segment = [(sec, val)]
        if current_segment:
            segments.append(current_segment)
        return segments

    # Get continuous segments
    segments = get_continuous_segments(df['Seconds'], mse_values)

    # Plot each segment separately
    for segment in segments:
        segment_seconds, segment_mse = zip(*segment)
        ax.scatter(segment_seconds, segment_mse, color=color, alpha=0.3, s=5)
        
        # Calculate and plot rolling mean and std for this segment
        if len(segment) > 1:  # Only if there's more than one point in the segment
            segment_df = pd.DataFrame({'Seconds': segment_seconds, 'MSE': segment_mse})
            segment_df = segment_df.sort_values('Seconds')
            mean = segment_df['MSE'].rolling(window=min(10, len(segment)), min_periods=1, center=True).mean()
            std = segment_df['MSE'].rolling(window=min(10, len(segment)), min_periods=1, center=True).std()
            
            ax.plot(segment_df['Seconds'], mean, color=color, linewidth=0.5)
            ax.fill_between(segment_df['Seconds'], mean - std, mean + std, color=color, alpha=0.1)

    # Rest of the function remains the same
    median = np.median(mse_values)
    ax.axhline(y=median, color='black', linestyle='--', label='Median Baseline')

    threshold = np.mean(mse_values) + anomaly_threshold * np.std(mse_values)
    ax.axhline(y=threshold, color='red', linestyle='--', label=f'Anomaly Threshold')
    ax.text(ax.get_xlim()[1], threshold, f'Anomaly Threshold', verticalalignment='center', horizontalalignment='left', color='red')

    anomalies = determine_anomalies(mse_values, anomaly_threshold)
    anomaly_frames = df['Frame'].iloc[anomalies].tolist()

    ax.scatter(df['Seconds'].iloc[anomalies], mse_values[anomalies], color='red', s=20, zorder=5)

    anomaly_data = list(zip(df['Timecode'].iloc[anomalies],
                            df['Seconds'].iloc[anomalies],
                            mse_values[anomalies]))
    anomaly_data.sort(key=lambda x: x[1])

    grouped_anomalies = []
    current_group = []
    for timecode, sec, mse in anomaly_data:
        if not current_group or sec - current_group[-1][1] <= time_threshold:
            current_group.append((timecode, sec, mse))
        else:
            grouped_anomalies.append(current_group)
            current_group = [(timecode, sec, mse)]
    if current_group:
        grouped_anomalies.append(current_group)

    for group in grouped_anomalies:
        start_sec = group[0][1]
        end_sec = group[-1][1]
        rect = Rectangle((start_sec, ax.get_ylim()[0]), end_sec - start_sec, ax.get_ylim()[1] - ax.get_ylim()[0],
                         facecolor='red', alpha=0.2, zorder=1)
        ax.add_patch(rect)

    for group in grouped_anomalies:
        highest_mse_anomaly = max(group, key=lambda x: x[2])
        timecode, sec, mse = highest_mse_anomaly
        ax.annotate(timecode, (sec, mse), textcoords="offset points", xytext=(0, 10),
                    ha='center', fontsize=6, color='red')

    max_seconds = df['Seconds'].max()
    num_ticks = 100
    tick_locations = np.linspace(0, max_seconds, num_ticks)
    tick_labels = [seconds_to_timecode(int(s)) for s in tick_locations]

    ax.set_xticks(tick_locations)
    ax.set_xticklabels(tick_labels, rotation=90, ha='center', fontsize=6)

    ax.set_xlabel('Timecode')
    ax.set_ylabel('Mean Squared Error')
    ax.set_title(title)

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

def plot_combined_mse(df, mse_embeddings, mse_posture, mse_voice, title, anomaly_threshold=4, time_threshold=3):
    plt.figure(figsize=(16, 8), dpi=300)
    fig, ax = plt.subplots(figsize=(16, 8))

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

    # Function to identify continuous segments
    def get_continuous_segments(seconds, values, max_gap=1):
        segments = []
        current_segment = []
        for i, (sec, val) in enumerate(zip(seconds, values)):
            if not current_segment or (sec - current_segment[-1][0] <= max_gap):
                current_segment.append((sec, val))
            else:
                segments.append(current_segment)
                current_segment = [(sec, val)]
        if current_segment:
            segments.append(current_segment)
        return segments

    # Scale all MSE values to the same range (0 to 1)
    def scale_mse(mse_values):
        min_val = np.min(mse_values)
        max_val = np.max(mse_values)
        return (mse_values - min_val) / (max_val - min_val)

    mse_embeddings_scaled = scale_mse(mse_embeddings)
    mse_posture_scaled = scale_mse(mse_posture)
    mse_voice_scaled = scale_mse(mse_voice)

    # Plot each data series
    for mse_values, color, label in zip([mse_embeddings_scaled, mse_posture_scaled, mse_voice_scaled], 
                                        ['navy', 'purple', 'green'], 
                                        ['Facial Features', 'Body Posture', 'Voice']):
        segments = get_continuous_segments(df['Seconds'], mse_values)
        
        for segment in segments:
            segment_seconds, segment_mse = zip(*segment)
            ax.scatter(segment_seconds, segment_mse, color=color, alpha=0.3, s=5, label=label if segment == segments[0] else "")
            
            if len(segment) > 1:
                segment_df = pd.DataFrame({'Seconds': segment_seconds, 'MSE': segment_mse})
                segment_df = segment_df.sort_values('Seconds')
                mean = segment_df['MSE'].rolling(window=min(10, len(segment)), min_periods=1, center=True).mean()
                std = segment_df['MSE'].rolling(window=min(10, len(segment)), min_periods=1, center=True).std()
                
                ax.plot(segment_df['Seconds'], mean, color=color, linewidth=0.5)
                ax.fill_between(segment_df['Seconds'], mean - std, mean + std, color=color, alpha=0.1)

        # Plot median baseline for each series
        median = np.median(mse_values)
        ax.axhline(y=median, color=color, linestyle=':', alpha=0.5, label=f'{label} Baseline Median')

        # Plot threshold for each series
        threshold = np.mean(mse_values) + anomaly_threshold * np.std(mse_values)
        ax.axhline(y=threshold, color=color, linestyle='--', alpha=1, label=f'{label} Anomaly Threshold')

        # Plot anomalies in red
        anomalies = mse_values > threshold
        ax.scatter(df['Seconds'][anomalies], mse_values[anomalies], color='red', s=20, zorder=5)

    max_seconds = df['Seconds'].max()
    num_ticks = 100
    tick_locations = np.linspace(0, max_seconds, num_ticks)
    tick_labels = [seconds_to_timecode(int(s)) for s in tick_locations]

    ax.set_xticks(tick_locations)
    ax.set_xticklabels(tick_labels, rotation=90, ha='center', fontsize=6)

    ax.set_xlabel('Timecode')
    ax.set_ylabel('Scaled Mean Squared Error')
    ax.set_title(title)

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

def plot_mse_histogram(mse_values, title, anomaly_threshold, color='blue'):
    plt.figure(figsize=(16, 3), dpi=300)
    fig, ax = plt.subplots(figsize=(16, 3))

    ax.hist(mse_values, bins=100, edgecolor='black', color=color, alpha=0.7)
    ax.set_xlabel('Mean Squared Error')
    ax.set_ylabel('Number of Frames')
    ax.set_title(title)

    mean = np.mean(mse_values)
    std = np.std(mse_values)
    threshold = mean + anomaly_threshold * std

    ax.axvline(x=threshold, color='red', linestyle='--', linewidth=2)

    plt.tight_layout()
    plt.close()
    return fig


def plot_mse_heatmap(mse_values, title, df):
    plt.figure(figsize=(20, 3), dpi=300)
    fig, ax = plt.subplots(figsize=(20, 3))

    # Reshape MSE values to 2D array for heatmap
    mse_2d = mse_values.reshape(1, -1)

    # Create heatmap
    sns.heatmap(mse_2d, cmap='YlOrRd', cbar=False, ax=ax)

    # Set x-axis ticks to timecodes
    num_ticks = min(60, len(mse_values))
    tick_locations = np.linspace(0, len(mse_values) - 1, num_ticks).astype(int)
    
    # Ensure tick_locations are within bounds
    tick_locations = tick_locations[tick_locations < len(df)]
    
    tick_labels = [df['Timecode'].iloc[i] if i < len(df) else '' for i in tick_locations]

    ax.set_xticks(tick_locations)
    ax.set_xticklabels(tick_labels, rotation=90, ha='center', va='top')

    ax.set_title(title)

    # Remove y-axis labels
    ax.set_yticks([])

    plt.tight_layout()
    plt.close()
    return fig

def plot_posture(df, posture_scores, color='blue', anomaly_threshold=3):
    plt.figure(figsize=(16, 8), dpi=300)
    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(':')))))

    posture_data = [(frame, score) for frame, score in posture_scores.items() if score is not None]
    posture_frames, posture_scores = zip(*posture_data)

    # Create a new dataframe for posture data
    posture_df = pd.DataFrame({'Frame': posture_frames, 'Score': posture_scores})


    posture_df = posture_df.merge(df[['Frame', 'Seconds']], on='Frame', how='inner')

    ax.scatter(posture_df['Seconds'], posture_df['Score'], color=color, alpha=0.3, s=5)
    mean = posture_df['Score'].rolling(window=10).mean()
    ax.plot(posture_df['Seconds'], mean, color=color, linewidth=0.5)

    ax.set_xlabel('Timecode')
    ax.set_ylabel('Posture Score')
    ax.set_title("Body Posture Over Time")

    ax.grid(True, linestyle='--', alpha=0.7)

    max_seconds = df['Seconds'].max()
    num_ticks = 80
    tick_locations = np.linspace(0, max_seconds, num_ticks)
    tick_labels = [seconds_to_timecode(int(s)) for s in tick_locations]

    ax.set_xticks(tick_locations)
    ax.set_xticklabels(tick_labels, rotation=90, ha='center', fontsize=6)

    plt.tight_layout()
    plt.close()
    return fig

def create_heatmap(t, mse_embeddings, mse_posture, mse_voice, video_fps, total_frames, video_width):
    frame_count = int(t * video_fps)
    
    # Normalize MSE values
    mse_embeddings_norm = (mse_embeddings - np.min(mse_embeddings)) / (np.max(mse_embeddings) - np.min(mse_embeddings))
    mse_posture_norm = (mse_posture - np.min(mse_posture)) / (np.max(mse_posture) - np.min(mse_posture))
    mse_voice_norm = (mse_voice - np.min(mse_voice)) / (np.max(mse_voice) - np.min(mse_voice))
    
    combined_mse = np.zeros((3, total_frames))
    combined_mse[0] = mse_embeddings_norm
    combined_mse[1] = mse_posture_norm
    combined_mse[2] = mse_voice_norm

    fig, ax = plt.subplots(figsize=(video_width / 250, 0.6))
    ax.imshow(combined_mse, aspect='auto', cmap='Reds', vmin=0, vmax=1, extent=[0, total_frames, 0, 3])
    ax.set_yticks([0.5, 1.5, 2.5])
    ax.set_yticklabels(['Voice', 'Posture', 'Face'], fontsize=7) 
    ax.set_xticks([])

    ax.axvline(x=frame_count, color='black', linewidth=3)

    plt.tight_layout(pad=0.5)
    
    canvas = FigureCanvas(fig)
    canvas.draw()
    heatmap_img = np.frombuffer(canvas.tostring_rgb(), dtype='uint8')
    heatmap_img = heatmap_img.reshape(canvas.get_width_height()[::-1] + (3,))
    plt.close(fig)
    return heatmap_img

def create_video_with_heatmap(video_path, df, mse_embeddings, mse_posture, mse_voice, output_folder, desired_fps, largest_cluster):
    print(f"Creating heatmap video. Output folder: {output_folder}")
    
    os.makedirs(output_folder, exist_ok=True)
    
    output_filename = os.path.basename(video_path).rsplit('.', 1)[0] + '_heatmap.mp4'
    heatmap_video_path = os.path.join(output_folder, output_filename)
    
    print(f"Heatmap video will be saved at: {heatmap_video_path}")

    # Load the original video
    video = VideoFileClip(video_path)
    
    # Get video properties
    width, height = video.w, video.h
    total_frames = int(video.duration * video.fps)
    
    # Ensure all MSE arrays have the same length as total_frames
    mse_embeddings = np.interp(np.linspace(0, len(mse_embeddings) - 1, total_frames), 
                               np.arange(len(mse_embeddings)), mse_embeddings)
    mse_posture = np.interp(np.linspace(0, len(mse_posture) - 1, total_frames), 
                            np.arange(len(mse_posture)), mse_posture)
    mse_voice = np.interp(np.linspace(0, len(mse_voice) - 1, total_frames), 
                          np.arange(len(mse_voice)), mse_voice)
    
    def combine_video_and_heatmap(t):
        video_frame = video.get_frame(t)
        heatmap_frame = create_heatmap(t, mse_embeddings, mse_posture, mse_voice, video.fps, total_frames, width)
        heatmap_frame_resized = cv2.resize(heatmap_frame, (width, heatmap_frame.shape[0]))
        combined_frame = np.vstack((video_frame, heatmap_frame_resized))
        return combined_frame

    final_clip = VideoClip(combine_video_and_heatmap, duration=video.duration)
    final_clip = final_clip.set_audio(video.audio)

    # Write the final video
    final_clip.write_videofile(heatmap_video_path, codec='libx264', audio_codec='aac', fps=video.fps)
    
    # Close the video clips
    video.close()
    final_clip.close()
    
    if os.path.exists(heatmap_video_path):
        print(f"Heatmap video created at: {heatmap_video_path}")
        print(f"Heatmap video size: {os.path.getsize(heatmap_video_path)} bytes")
        return heatmap_video_path
    else:
        print(f"Failed to create heatmap video at: {heatmap_video_path}")
        return None


# Function to create the correlation heatmap
def plot_correlation_heatmap(mse_embeddings, mse_posture, mse_voice):
    data = np.vstack((mse_embeddings, mse_posture, mse_voice)).T
    df = pd.DataFrame(data, columns=["Facial Features", "Body Posture", "Voice"])
    corr = df.corr()

    plt.figure(figsize=(10, 8), dpi=300)

    heatmap = sns.heatmap(corr, annot=True, cmap='coolwarm', vmin=-1, vmax=1)
    plt.title('Correlation Heatmap of MSEs')
    plt.tight_layout()
    return plt.gcf()