import cv2
import mediapipe as mp
import numpy as np
import math, time, json
from pythonosc import udp_client

# -------------------------------
# SETTINGS
# -------------------------------
TOUCH_CAM_INDEX = 1
GESTURE_CAM_INDEX = 0

GAME_SCREEN_WIDTH  = 900    #900
GAME_SCREEN_HEIGHT = 600    #600

STILL_REQUIRED = 1.0
MOVE_TOLERANCE = 25

# -------------------------------
# CAMERA / PERFORMANCE SETTINGS
# -------------------------------
CAMERA_FPS = 15
DISPLAY_WIDTH  = 320   #1280
DISPLAY_HEIGHT = 240    #720

MODEL_COMPLEXITY = 0   # ✅ 0=fast | 1=balanced | 2=accurate

client = udp_client.SimpleUDPClient("127.0.0.1", 5005)

# -------------------------------
# GLOBAL STATES
# -------------------------------
last_finger_pos = None
finger_still_start = None
prev_touch_time = 0.0
prev_clap_time = 0.0

# -------------------------------
# CALIBRATION + HOMOGRAPHY
# -------------------------------
try:
    with open("calibration.json", "r") as f:
        CALIB_POINTS = json.load(f)
    print("📐 Calibration loaded:", CALIB_POINTS)
except:
    CALIB_POINTS = None
    print("⚠️ No calibration found")

H = None
if CALIB_POINTS is not None:
    src = np.array(CALIB_POINTS, dtype=np.float32)
    dst = np.array([
        [0, 0],
        [GAME_SCREEN_WIDTH, 0],
        [GAME_SCREEN_WIDTH, GAME_SCREEN_HEIGHT],
        [0, GAME_SCREEN_HEIGHT]
    ], dtype=np.float32)
    H, _ = cv2.findHomography(src, dst)
    print("📐 Homography ready")


def map_point_homography(x, y):
    if H is None:
        return int(x), int(y)
    p = np.array([[[x, y]]], dtype=np.float32)
    mapped = cv2.perspectiveTransform(p, H)[0][0]
    return int(mapped[0]), int(mapped[1])

# -----------------------------------------------------------------

def run_gesture_input():
    global last_finger_pos, finger_still_start
    global prev_touch_time, prev_clap_time

    mp_hands = mp.solutions.hands
    mp_draw = mp.solutions.drawing_utils

    # ✅ model_complexity applied here
    hands_touch = mp_hands.Hands(
        max_num_hands=1,
        model_complexity=MODEL_COMPLEXITY,
        min_detection_confidence=0.6,
        min_tracking_confidence=0.6
    )

    hands_gesture = mp_hands.Hands(
        max_num_hands=2,
        model_complexity=MODEL_COMPLEXITY,
        min_detection_confidence=0.6,
        min_tracking_confidence=0.6
    )

    cam_touch   = cv2.VideoCapture(TOUCH_CAM_INDEX)
    cam_gesture = cv2.VideoCapture(GESTURE_CAM_INDEX)

    for cam in (cam_touch, cam_gesture):
        cam.set(cv2.CAP_PROP_FRAME_WIDTH, DISPLAY_WIDTH)
        cam.set(cv2.CAP_PROP_FRAME_HEIGHT, DISPLAY_HEIGHT)
        cam.set(cv2.CAP_PROP_FPS, CAMERA_FPS)

    clap_cooldown = 1.5
    frame_duration = 1.0 / CAMERA_FPS
    last_frame_time = time.time()

    while True:
        ok1, frame_touch = cam_touch.read()
        ok2, frame_gest  = cam_gesture.read()
        if not ok1 or not ok2:
            break

        frame_touch = cv2.flip(frame_touch, -1)
        frame_gest  = cv2.flip(frame_gest, 1)

        # ---------------- TOUCH ----------------
        res_t = hands_touch.process(cv2.cvtColor(frame_touch, cv2.COLOR_BGR2RGB))
        th, tw, _ = frame_touch.shape

        if res_t.multi_hand_landmarks:
            lm = res_t.multi_hand_landmarks[0]
            mp_draw.draw_landmarks(frame_touch, lm, mp_hands.HAND_CONNECTIONS)

            if lm.landmark[8].y < lm.landmark[5].y:
                last_finger_pos = None
                finger_still_start = None
                continue

            fx = int(lm.landmark[8].x * tw)
            fy = int(lm.landmark[8].y * th)
            sx, sy = map_point_homography(fx, fy)

            now = time.time()
            curr = (fx, fy)

            if last_finger_pos is None:
                last_finger_pos = curr
                finger_still_start = now
            else:
                dist = math.hypot(curr[0]-last_finger_pos[0],
                                  curr[1]-last_finger_pos[1])
                if dist < MOVE_TOLERANCE:
                    if finger_still_start and now-finger_still_start >= STILL_REQUIRED:
                        if now-prev_touch_time > 0.5:
                            client.send_message("/touch", [sx, sy])
                            print(f"👉 TOUCH {sx},{sy}")
                            prev_touch_time = now
                            finger_still_start = None
                else:
                    finger_still_start = now

                last_finger_pos = curr

            cv2.circle(frame_touch, (fx, fy), 10, (0,255,0), -1)

        else:
            last_finger_pos = None
            finger_still_start = None

        # ---------------- CLAP ----------------
        res_g = hands_gesture.process(cv2.cvtColor(frame_gest, cv2.COLOR_BGR2RGB))
        gh, gw, _ = frame_gest.shape

        if res_g.multi_hand_landmarks and len(res_g.multi_hand_landmarks) == 2:
            h1, h2 = res_g.multi_hand_landmarks
            x1 = np.mean([p.x for p in h1.landmark]) * gw
            y1 = np.mean([p.y for p in h1.landmark]) * gh
            x2 = np.mean([p.x for p in h2.landmark]) * gw
            y2 = np.mean([p.y for p in h2.landmark]) * gh
            dist = math.hypot(x2-x1, y2-y1)

            if dist < 100 and time.time()-prev_clap_time > clap_cooldown:
                prev_clap_time = time.time()
                client.send_message("/clap", 1)
                print("👏 CLAP")

        cv2.putText(frame_touch,
                    f"FPS:{CAMERA_FPS}  MC:{MODEL_COMPLEXITY}",
                    (10,30), cv2.FONT_HERSHEY_SIMPLEX,
                    0.8, (255,255,0), 2)

        cv2.imshow("Touch-Cam", frame_touch)
        cv2.imshow("Gesture-Cam", frame_gest)

        # FPS limiter
        sleep = frame_duration - (time.time()-last_frame_time)
        if sleep > 0:
            time.sleep(sleep)
        last_frame_time = time.time()

        if cv2.waitKey(1) & 0xFF == 27:
            break

    cam_touch.release()
    cam_gesture.release()
    cv2.destroyAllWindows()


if __name__ == "__main__":
    run_gesture_input()