import argparse
from scipy.spatial import distance as dist
from imutils import face_utils
import numpy as np
import imutils
import time
import dlib
import cv2
import matplotlib.pyplot as plt
from keras.preprocessing.image import img_to_array
from keras.models import load_model

def eye_brow_distance(leye, reye):
    global points
    distq = dist.euclidean(leye, reye)
    points.append(int(distq))
    return distq

def emotion_finder(faces, frame):
    global emotion_classifier
    EMOTIONS = ["angry", "disgust", "scared", "happy", "sad", "surprised", "neutral"]
    x, y, w, h = face_utils.rect_to_bb(faces)
    frame = frame[y:y + h, x:x + w]
    roi = cv2.resize(frame, (64, 64))
    roi = roi.astype("float") / 255.0
    roi = img_to_array(roi)
    roi = np.expand_dims(roi, axis=0)
    preds = emotion_classifier.predict(roi)[0]
    emotion_probability = np.max(preds)
    label = EMOTIONS[preds.argmax()]
    return label

def normalize_values(points, disp):
    normalized_value = abs(disp - np.min(points)) / abs(np.max(points) - np.min(points))
    stress_value = np.exp(-(normalized_value))
    return stress_value

def stress(video_path, duration):
    global points, emotion_classifier
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor("models/data")
    emotion_classifier = load_model("models/_mini_XCEPTION.102-0.66.hdf5", compile=False)
    
    cap = cv2.VideoCapture(video_path)
    points = []
    stress_labels = []
    start_time = time.time()

    while True:
        current_time = time.time()
        if current_time - start_time >= duration:
            break

        ret, frame = cap.read()
        if not ret:
            break
        
        frame = cv2.flip(frame, 1)
        frame = imutils.resize(frame, width=500, height=500)

        (lBegin, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eyebrow"]
        (rBegin, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eyebrow"]

        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        try:
            detections = detector(gray, 0)
            for detection in detections:
                emotion = emotion_finder(detection, gray)
                shape = predictor(gray, detection)
                shape = face_utils.shape_to_np(shape)

                leyebrow = shape[lBegin:lEnd]
                reyebrow = shape[rBegin:rEnd]

                distq = eye_brow_distance(leyebrow[-1], reyebrow[0])
                stress_value = normalize_values(points, distq)

                # Determine stress label for this frame
                if emotion in ['scared', 'sad', 'angry'] and stress_value >= 0.75:
                    stress_label = 'stressed'
                else:
                    stress_label = 'not stressed'

                # Store stress label in list
                stress_labels.append(stress_label)

        except Exception as e:
            print(f'Error: {e}')

        key = cv2.waitKey(1) & 0xFF
        if key == ord('q'):
            break

    cap.release()

    # Count occurrences of 'stressed' and 'not stressed'
    stressed_count = stress_labels.count('stressed')
    not_stressed_count = stress_labels.count('not stressed')

    # Determine which label occurred more frequently
    if stressed_count > not_stressed_count:
        most_frequent_label = 'stressed'
    else:
        most_frequent_label = 'not stressed'

    return stressed_count, not_stressed_count, most_frequent_label

def main():
    # Argument parsing
    parser = argparse.ArgumentParser(description='Stress Detection from Video')
    parser.add_argument('--video', type=str, required=True, default='output.mp4', help='Path to the input video file')
    parser.add_argument('--duration', type=int, default=30, help='Duration for analysis in seconds')
    args = parser.parse_args()

    # Call the stress function and get the results
    stressed_count, not_stressed_count, most_frequent_label = stress(args.video, args.duration)

    # Display the result
    print(f"Stressed frames: {stressed_count}")
    print(f"Not stressed frames: {not_stressed_count}")
    print(f"Most frequent state: {most_frequent_label}")

if __name__ == '__main__':
    main()