import numpy as np
import json
import cv2

from dataclasses import dataclass, field, asdict
from typing import List, Optional, Tuple, Dict


# ----- Helper Functions -----
def compute_distance(p1, p2):
    return np.hypot(p2[0] - p1[0], p2[1] - p1[1])

# 4) compute straightness per finger
def finger_straight(i_tip, i_pip, i_mcp):
    d1 = np.hypot(*(np.array(i_tip)-np.array(i_pip)))
    d2 = np.hypot(*(np.array(i_pip)-np.array(i_mcp)))
    return float(np.clip(d1/(d2+1e-3), 0, 1))


def default_profile():
    return []

@dataclass
class CameraObject():
    capture: Optional[cv2.VideoCapture]
    index: int
    pxHeight: int = 1536
    pxWidth: int = 2048

    # Calibration
    camera_matrix: Optional[np.ndarray] = None          # 3x3
    distortion_coefficients: Optional[np.ndarray] = None       # 1x5 / 1x8
    rotation_matrix_world_to_camera: Optional[np.ndarray] = None          # 3x3 (world->cam)
    translation_vector_world_to_camera: Optional[np.ndarray] = None          # 3x1 (world->cam)
    camera_projection_matrix: Optional[np.ndarray] = None

    def load_video_capture(self):
        #self.capture = cv2.VideoCapture(self.index, cv2.CAP_MSMF)
        self.capture = cv2.VideoCapture(self.index, cv2.CAP_ANY)
        #self.capture = cv2.VideoCapture(self.index, cv2.CAP_DSHOW)
        self.capture.set(cv2.CAP_PROP_FRAME_WIDTH, 2048.0)
        self.capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1536.0)
 

@dataclass
class Hand2D():
    camera_id: str                   # e.g. "overhead_left"
    handedness: str                  # "Left" / "Right"
    score: float                     # detection/tracking confidence
    landmarks_px: np.ndarray         # shape (21,2) in pixels


@dataclass
class CharucoDetection:
    camera_id: int
    
    # Output of Charuco detection
    charuco_corners: np.ndarray    # (N, 1, 2) float32
    charuco_ids: np.ndarray        # (N, 1) int32

    gray_frame: Optional[np.ndarray] = None


@dataclass
class CalibrationSample:
    detections: dict[int, CharucoDetection]  # camera_id → detection