functions/helper.py

import cv2
import numpy as np 
import dlib
from tqdm import tqdm
from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib.units import inch
from reportlab.platypus import SimpleDocTemplate, Paragraph, Table, TableStyle, Spacer,Image
from io import BytesIO
import matplotlib.pyplot as plt 
def extract_face(image, net, predictor):    
    (h, w) = image.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
    net.setInput(blob)
    detections = net.forward()
    for i in range(0, detections.shape[2]):
        confidence = detections[0, 0, i, 2]

        # Filter out weak detections
        if confidence > 0.5:
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # Convert bounding box to dlib rectangle format
            dlib_rect = dlib.rectangle(int(startX), int(startY), int(endX), int(endY))
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            landmarks = predictor(gray, dlib_rect)  
            landmarks_np = np.array([[p.x, p.y] for p in landmarks.parts()])
            x, y, w, h = cv2.boundingRect(landmarks_np)
            x -= 25
            y -= 25
            w += 50
            h += 50

            x = max(0, x)
            y = max(0, y)
            w = min(w, image.shape[1] - x)
            h = min(h, image.shape[0] - y)
            face_crop=image[y:y+h,x:x+w]
            # Crop and resize the face
            try:
                face_crop = cv2.resize(face_crop, (224, 224))
            except:
                face_crop = cv2.resize(image, (224, 224))
            return face_crop,landmarks_np,(w,h)
    return None,None,None

def extract_faces_from_frames(frames, net, predictor):
    faces_list = []
    landmarks_list = []
    sizes_list = []

    for image in tqdm(frames):
        face_crop, landmarks_np, size = extract_face(image, net, predictor)
        
        # Append the results to the respective lists
        faces_list.append(face_crop)
        landmarks_list.append(landmarks_np)
        sizes_list.append(size)

    return faces_list, landmarks_list, sizes_list

def make_pdf(file_path,data,buf,buf2):
    doc = SimpleDocTemplate(file_path, pagesize=A4)

    # Define styles
    styles = getSampleStyleSheet()
    content = []

    # Adding title
    content.append(Paragraph("Facial Emotion Recognition Report", styles['Title']))
    content.append(Spacer(1, 12))

    # Section 1: Facial Emotion Recognition
    content.append(Paragraph("Facial Emotion Recognition", styles['Heading2']))
    table_data = [["Emotion", "Frame Count"]]
    for emotion, count in data["facial_emotion_recognition"]["class_wise_frame_count"].items():
        table_data.append([emotion.capitalize(), str(count)])

    table = Table(table_data, hAlign='LEFT')
    table.setStyle(TableStyle([
        ('BACKGROUND', (0, 0), (-1, 0), colors.grey),
        ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
        ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
        ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
        ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
        ('BACKGROUND', (0, 1), (-1, -1), colors.beige),
        ('GRID', (0, 0), (-1, -1), 1, colors.black),
    ]))
    content.append(table)
    content.append(Spacer(1, 12))

    # Section 2: Audio Analysis
    content.append(Paragraph("Audio Analysis", styles['Heading2']))
    content.append(Paragraph(f"Transcript: {data['audio']['transcript']}", styles['BodyText']))

    sentiment = data['audio']['sentiment'][0]
    content.append(Paragraph(f"Sentiment: {sentiment['label']} (Score: {sentiment['score']})", styles['BodyText']))

    audio_features = [
        f"Video Duration:{data['duration']}",
        f"Sound Intensity: {data['audio']['sound_intensity']}",
        f"Fundamental Frequency: {data['audio']['fundamental_frequency']}",
        f"Spectral Energy: {data['audio']['spectral_energy']}",
        f"Spectral Centroid: {data['audio']['spectral_centroid']}",
        f"Zero Crossing Rate: {data['audio']['zero_crossing_rate']}",
        f"Average Words per Minute: {data['audio']['avg_words_per_minute'] if data['duration']>60 else -1}",
        f"Average Unique Words per Minute: {data['audio']['avg_unique_words_per_minute'] if data['duration']>60 else -1}",
        f"Unique Word Count: {data['audio']['unique_word_count']}",
        f"Filler Words per Minute: {data['audio']['filler_words_per_minute']}",
        f"Noun Count: {data['audio']['noun_count']}",
        f"Adjective Count: {data['audio']['adjective_count']}",
        f"Verb Count: {data['audio']['verb_count']}",
        f"Pause Rate: {data['audio']['pause_rate']}"
    ]
    
    for feature in audio_features:
        content.append(Paragraph(feature, styles['BodyText']))
    content.append(Spacer(1, 12))

    plot_image = Image(buf)
    plot_image.drawHeight = 600  # Adjust height
    plot_image.drawWidth = 600   # Adjust width
    content.append(plot_image)
    plot_image = Image(buf2)
    plot_image.drawHeight = 600  # Adjust height
    plot_image.drawWidth = 600   # Adjust width
    content.append(plot_image)
    # Build the PDF
    doc.build(content)



def plot_facial_expression_graphs(smile_data, ear_data, yawn_data, thresholds, path):
    """
    Plots multiple subplots (smile, EAR, and yawn ratios) in one figure.
    
    Parameters:
    - smile_data: List of smile ratios.
    - ear_data: List of eye aspect ratios (EAR).
    - yawn_data: List of yawn ratios.
    - thresholds: List containing thresholds for smile, EAR, and yawn.
    - path: Path to save the combined plot.
    
    Returns:
    - buf: BytesIO buffer containing the saved plot.
    """
    buf = BytesIO()
    plt.figure(figsize=(12, 8))  # Create a figure of appropriate size

    # Plot smile data
    plt.subplot(3, 1, 1)
    plt.plot(smile_data, label='Smile Ratio (Width/Face Width)')
    plt.axhline(y=thresholds[0], color='black', linestyle='--', label='Threshold')
    plt.title('Smile Ratio Over Time')
    plt.ylabel('Ratio')
    plt.legend()

    # Plot EAR data
    plt.subplot(3, 1, 2)
    plt.plot(ear_data, label='Eye Aspect Ratio (EAR)', color='orange')
    plt.axhline(y=thresholds[1], color='black', linestyle='--', label='Threshold')
    plt.title('Eye Aspect Ratio (EAR) Over Time')
    plt.ylabel('Ratio')
    plt.legend()

    # Plot yawn data
    plt.subplot(3, 1, 3)
    plt.plot(yawn_data, label='Yawn Ratio (Mouth Height/Face Height)', color='red')
    plt.axhline(y=thresholds[2], color='black', linestyle='--', label='Threshold')
    plt.title('Yawn Ratio Over Time')
    plt.xlabel('Frames')
    plt.ylabel('Ratio')
    plt.legend()

    plt.tight_layout()  # Adjust layout to prevent overlap
    plt.savefig(buf, format='png')  # Save to buffer
    plt.clf()  # Clear the figure after saving
    buf.seek(0)  # Rewind the buffer to the beginning
    return buf