import cv2
import numpy as np
import json
import mediapipe as mp
import math

# Initialize MediaPipe
mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles

hands = mp_hands.Hands(
    static_image_mode=True,
    max_num_hands=1,
    min_detection_confidence=0.7 # Increased confidence threshold
)

# Load Rules
with open("gesture_rules.json", "r") as f:
    GESTURE_DATA = json.load(f)["gestures"]

def calculate_distance(p1, p2):
    """Calculates Euclidean distance between two 3D landmarks."""
    return math.sqrt((p1.x - p2.x)**2 + (p1.y - p2.y)**2 + (p1.z - p2.z)**2)

def get_finger_states_robust(landmarks):
    """
    Returns [thumb, index, middle, ring, pinky] states (1=Open, 0=Closed)
    using Euclidean distance relative to the Wrist (Landmark 0).
    """
    states = []
    wrist = landmarks[0]

    # --- FINGERS (Index, Middle, Ring, Pinky) ---
    # Logic: If Distance(Tip, Wrist) < Distance(PIP, Wrist), the finger is curled.
    
    # Index (Tip 8, PIP 6)
    states.append(1 if calculate_distance(landmarks[8], wrist) > calculate_distance(landmarks[6], wrist) else 0)
    
    # Middle (Tip 12, PIP 10)
    states.append(1 if calculate_distance(landmarks[12], wrist) > calculate_distance(landmarks[10], wrist) else 0)

    # Ring (Tip 16, PIP 14)
    states.append(1 if calculate_distance(landmarks[16], wrist) > calculate_distance(landmarks[14], wrist) else 0)

    # Pinky (Tip 20, PIP 18)
    states.append(1 if calculate_distance(landmarks[20], wrist) > calculate_distance(landmarks[18], wrist) else 0)

    # --- THUMB LOGIC (Complex) ---
    # The thumb doesn't curl to the wrist like other fingers.
    # We check if the Tip (4) is further away from the Pinky MCP (17) than the IP (3) is.
    # Basically, is the thumb sticking out?
    
    dist_tip_pinky = calculate_distance(landmarks[4], landmarks[17])
    dist_ip_pinky = calculate_distance(landmarks[3], landmarks[17])
    
    # We insert Thumb at the start of the list to match JSON format
    if dist_tip_pinky > dist_ip_pinky:
        states.insert(0, 1) # Open
    else:
        states.insert(0, 0) # Closed

    return states

def classify_gesture(image: np.ndarray):
    """
    Returns: (gesture_name, annotated_image)
    """
    # Convert image to writable for MediaPipe
    image.flags.writeable = False
    results = hands.process(image)
    image.flags.writeable = True

    # If no hand found
    if not results.multi_hand_landmarks:
        return None, image

    # Get landmarks for the first hand
    hand_landmarks = results.multi_hand_landmarks[0]
    
    # Draw the Skeleton on the image
    annotated_image = image.copy()
    mp_drawing.draw_landmarks(
        annotated_image,
        hand_landmarks,
        mp_hands.HAND_CONNECTIONS,
        mp_drawing_styles.get_default_hand_landmarks_style(),
        mp_drawing_styles.get_default_hand_connections_style()
    )

    # Get binary states
    states = get_finger_states_robust(hand_landmarks.landmark)
    print(f"Detected States (T,I,M,R,P): {states}")

    # Match against DB
    detected_gesture = "UNKNOWN"
    for name, info in GESTURE_DATA.items():
        if states == info["pattern"]:
            detected_gesture = name
            break
            
    return detected_gesture, annotated_image