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_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 filter_mse_for_most_frequent_person(df, mse_embeddings, mse_posture, mse_voice, most_frequent_person_frames):
    # Ensure most_frequent_person_frames is a list
    if not isinstance(most_frequent_person_frames, (list, np.ndarray)):
        most_frequent_person_frames = [most_frequent_person_frames]

    # Ensure df and mse arrays have the same length
    min_length = min(len(df), len(mse_embeddings), len(mse_posture), len(mse_voice))
    df = df.iloc[:min_length].copy()
    mse_embeddings = mse_embeddings[:min_length]
    mse_posture = mse_posture[:min_length]
    mse_voice = mse_voice[:min_length]

    # Create a mask for the most frequent person frames
    mask = df['Frame'].isin(most_frequent_person_frames)

    # Pad mask to match the length of the video frames
    padded_mask = np.zeros(len(mse_embeddings), dtype=bool)
    padded_mask[:len(mask)] = mask

    # Apply the mask to filter the MSE arrays
    mse_embeddings_filtered = np.where(padded_mask, mse_embeddings, 0)
    mse_posture_filtered = np.where(padded_mask, mse_posture, 0)
    mse_voice_filtered = np.where(padded_mask, mse_voice, 0)

    return mse_embeddings_filtered, mse_posture_filtered, mse_voice_filtered

def create_video_with_heatmap(video_path, df, mse_embeddings, mse_posture, mse_voice, output_folder, desired_fps, most_frequent_person_frames):
    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)
    
    # Filter MSE arrays for the most frequent person frames
    mse_embeddings_filtered, mse_posture_filtered, mse_voice_filtered = filter_mse_for_most_frequent_person(df, mse_embeddings, mse_posture, mse_voice, most_frequent_person_frames)
    
    def combine_video_and_heatmap(t):
        video_frame = video.get_frame(t)
        heatmap_frame = create_heatmap(t, mse_embeddings_filtered, mse_posture_filtered, mse_voice_filtered, 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

def create_heatmap(t, mse_embeddings_filtered, mse_posture_filtered, mse_voice_filtered, fps, total_frames, width):
    # Normalize the MSE values
    mse_embeddings_norm = normalize_mse(mse_embeddings_filtered)
    mse_posture_norm = normalize_mse(mse_posture_filtered)
    mse_voice_norm = normalize_mse(mse_voice_filtered)

    # Debug prints
    print(f"mse_embeddings_norm shape: {mse_embeddings_norm.shape}")
    print(f"mse_posture_norm shape: {mse_posture_norm.shape}")
    print(f"mse_voice_norm shape: {mse_voice_norm.shape}")

    # Ensure combined_mse has the correct shape
    combined_mse = np.zeros((total_frames, width))

    # Adjust shapes and pad with zeros if necessary
    mse_embeddings_norm = pad_or_trim_array(mse_embeddings_norm, width)
    mse_posture_norm = pad_or_trim_array(mse_posture_norm, width)
    mse_voice_norm = pad_or_trim_array(mse_voice_norm, width)

    combined_mse[0] = mse_embeddings_norm
    # Assuming you combine posture and voice MSEs similarly
    combined_mse[1] = mse_posture_norm
    combined_mse[2] = mse_voice_norm

    # Return or use combined_mse as needed
    return combined_mse

def normalize_mse(mse):
    # Your normalization logic here
    return mse / np.max(mse)

def pad_or_trim_array(arr, target_length):
    if len(arr) > target_length:
        # Trim the array
        return arr[:target_length]
    elif len(arr) < target_length:
        # Pad the array with zeros
        return np.pad(arr, (0, target_length - len(arr)), 'constant')
    return arr

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()