visualization.py

import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg
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
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'Threshold: {anomaly_threshold:.1f}')
    ax.text(ax.get_xlim()[1], threshold, f'Threshold: {anomaly_threshold:.1f}', 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('N')
    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_video_with_heatmap(video_path, df, mse_embeddings, mse_posture, mse_voice, output_folder, desired_fps, largest_cluster, progress=None):
    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'
    temp_video_path = os.path.join(output_folder, 'temp_' + output_filename)
    heatmap_video_path = os.path.join(output_folder, output_filename)
    
    print(f"Heatmap video will be saved at: {heatmap_video_path}")

    cap = cv2.VideoCapture(video_path)
    original_fps = cap.get(cv2.CAP_PROP_FPS)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    
    fourcc = cv2.VideoWriter_fourcc(*'avc1')
    out = cv2.VideoWriter(temp_video_path, fourcc, original_fps, (width, height + 200))
    print(f"VideoWriter initialized. FPS: {original_fps}, Size: {(width, height + 200)}")
    
    # 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)
    
    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

    cdict = {
        'red':   [(0.0,  0.0, 0.0),
                  (1.0,  1.0, 1.0)],
        'green': [(0.0,  1.0, 1.0),
                  (1.0,  0.0, 0.0)],
        'blue':  [(0.0,  1.0, 1.0),
                  (1.0,  0.0, 0.0)]
    }

    custom_cmap = LinearSegmentedColormap('custom_cmap', segmentdata=cdict, N=256)
    
    fig, ax = plt.subplots(figsize=(width/100, 2))
    im = ax.imshow(combined_mse, aspect='auto', cmap=custom_cmap, extent=[0, total_frames, 0, 3])
    ax.set_yticks([0.5, 1.5, 2.5])
    ax.set_yticklabels(['Face', 'Posture', 'Voice'])
    ax.set_xticks([])
    plt.tight_layout()
    
    line = None
    try:
        for frame_count in range(total_frames):
            ret, frame = cap.read()
            if not ret:
                print(f"Failed to read frame {frame_count}")
                break
            
            if line:
                line.remove()
            line = ax.axvline(x=frame_count, color='r', linewidth=2)
            
            canvas = FigureCanvasAgg(fig)
            canvas.draw()
            heatmap_img = np.frombuffer(canvas.tostring_rgb(), dtype='uint8')
            heatmap_img = heatmap_img.reshape(canvas.get_width_height()[::-1] + (3,))
            heatmap_img = cv2.resize(heatmap_img, (width, 200))
            
            # Convert frame from BGR to RGB
            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            
            # Combine RGB frame with RGB heatmap
            combined_frame = np.vstack((frame_rgb, heatmap_img))
            
            seconds = frame_count / original_fps
            timecode = f"{int(seconds//3600):02d}:{int((seconds%3600)//60):02d}:{int(seconds%60):02d}"
            
            # Add timecode to the combined frame (which is now in RGB)
            combined_frame = cv2.putText(combined_frame, f"Time: {timecode}", (10, 30), 
                                         cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
            
            # Convert back to BGR for OpenCV VideoWriter
            combined_frame_bgr = cv2.cvtColor(combined_frame, cv2.COLOR_RGB2BGR)
            
            out.write(combined_frame_bgr)

            if progress is not None:
                try:
                    progress((frame_count + 1) / total_frames, desc="Generating video with heatmap")
                except Exception as e:
                    print(f"Error updating progress: {str(e)}")

    except Exception as e:
        print(f"Error in frame processing: {str(e)}")
        import traceback
        traceback.print_exc()
    finally:
        cap.release()
        out.release()
        plt.close(fig)

    # Add audio to the video
    try:
        original_video = VideoFileClip(video_path)
        heatmap_video = VideoFileClip(temp_video_path)
        
        # Resize the heatmap video to match the original video's duration
        heatmap_video = heatmap_video.set_duration(original_video.duration)
        
        # Add the audio from the original video
        final_video = heatmap_video.set_audio(original_video.audio)
        
        # Write the final video
        final_video.write_videofile(heatmap_video_path, codec='libx264', audio_codec='aac')
        
        # Close the video clips
        original_video.close()
        heatmap_video.close()
        final_video.close()
        
        # Remove the temporary video file
        os.remove(temp_video_path)
        
    except Exception as e:
        print(f"Error in adding audio to video: {str(e)}")
        import traceback
        traceback.print_exc()
    
    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