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 matplotlib.patches import Rectangle
from utils import seconds_to_timecode
from anomaly_detection import determine_anomalies
from scipy import interpolate
import gradio as gr

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('Number of Samples')
    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 = 60
    tick_locations = np.linspace(0, len(mse_values) - 1, num_ticks).astype(int)
    tick_labels = [df['Timecode'].iloc[i] 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_path, desired_fps, largest_cluster, progress=None):
    print(f"Creating heatmap video. Output path: {output_path}")
    # Filter the DataFrame to only include frames from the largest cluster
    df_largest_cluster = df[df['Cluster'] == largest_cluster]
    
    # Interpolate mse_voice to match the length of df
    x_voice = np.linspace(0, len(mse_voice)-1, len(mse_voice))
    x_new = np.linspace(0, len(mse_voice)-1, len(df))
    f = interpolate.interp1d(x_voice, mse_voice)
    mse_voice_interpolated = f(x_new)
    
    mse_embeddings = mse_embeddings[df['Cluster'] == largest_cluster]
    mse_posture = mse_posture[df['Cluster'] == largest_cluster]
    mse_voice_interpolated = mse_voice_interpolated[df['Cluster'] == largest_cluster]

    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(*'mp4v')
    out = cv2.VideoWriter(output_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_interpolated = np.interp(np.linspace(0, len(mse_voice_interpolated) - 1, total_frames), 
                                       np.arange(len(mse_voice_interpolated)), mse_voice_interpolated)
    
    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_interpolated - np.min(mse_voice_interpolated)) / (np.max(mse_voice_interpolated) - np.min(mse_voice_interpolated))
    
    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

    # Custom colormap definition
    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()
    
    if progress:
        progress(0, desc="Generating video with heatmap")

    line = None
    for frame_count in range(total_frames):
        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_count)
        ret, frame = cap.read()
        if not ret:
            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))
        
        combined_frame = np.vstack((frame, heatmap_img))
        
        seconds = frame_count / original_fps
        timecode = f"{int(seconds//3600):02d}:{int((seconds%3600)//60):02d}:{int(seconds%60):02d}"
        cv2.putText(combined_frame, f"Time: {timecode}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
        
        out.write(combined_frame)

        if progress:
            progress((frame_count + 1) / total_frames, desc="Generating video with heatmap")
    
    cap.release()
    out.release()
    plt.close(fig)
    print(f"Heatmap video created at: {output_path}")
    return output_path