import json
import os
import random
import numpy as np
from typing import List
import math
import cv2
import skeleton_lib as skel
import process_json_file as pjf
import sys
sys.path.append('./')

def is_normalized(keypoints: List[skel.Keypoint]) -> bool:
    for keypoint in keypoints:
        if not (0 <= keypoint.x <= 1 and 0 <= keypoint.y <= 1):
            return False
    return True

def draw_bodypose(canvas: np.ndarray, keypoints: List[skel.Keypoint], limbSeq, colors, xinsr_stick_scaling: bool = False) -> np.ndarray:
    """
    Draw keypoints and limbs representing body pose on a given canvas.

    Args:
        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the body pose.
        keypoints (List[Keypoint]): A list of Keypoint objects representing the body keypoints to be drawn.
        xinsr_stick_scaling (bool): Whether or not scaling stick width for xinsr ControlNet

    Returns:
        np.ndarray: A 3D numpy array representing the modified canvas with the drawn body pose.

    Note:
        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
    """
    if not is_normalized(keypoints):
        H, W = 1.0, 1.0
    else:
        H, W, _ = canvas.shape

    CH, CW, _ = canvas.shape
    stickwidth = 2

    # Ref: https://huggingface.co/xinsir/controlnet-openpose-sdxl-1.0
    max_side = max(CW, CH)
    if xinsr_stick_scaling:
        stick_scale = 1 if max_side < 500 else min(2 + (max_side // 1000), 7)
    else :
        stick_scale = 1
    
    if keypoints is None or len(keypoints) == 0:
        return canvas

    for (k1_index, k2_index), color in zip(limbSeq, colors):
        keypoint1 = keypoints[k1_index]
        keypoint2 = keypoints[k2_index]

        if keypoint1 is None or keypoint2 is None or keypoint1.confidence == 0 or keypoint2.confidence == 0:
            # if keypoint1 is None or keypoint1.confidence == 0:
            #     print(f"keypoint failed: {k1_index}")
            # if keypoint2 is None or keypoint2.confidence == 0:
            #     print(f"keypoint failed: {k2_index}")
            continue

        Y = np.array([keypoint1.x, keypoint2.x]) * float(W)
        X = np.array([keypoint1.y, keypoint2.y]) * float(H)
        mX = np.mean(X)
        mY = np.mean(Y)
        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
        polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth*stick_scale), int(angle), 0, 360, 1)
        cv2.fillConvexPoly(canvas, polygon, [int(float(c) * 0.6) for c in color])

    for keypoint, color in zip(keypoints, colors):
        if keypoint is None or keypoint.confidence == 0:
            continue

        x, y = keypoint.x, keypoint.y
        x = int(x * W)
        y = int(y * H)
        cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1)

    return canvas

def coordinates_to_keypoints(coordinates: list) -> List[skel.Keypoint]:
    keypoints = [skel.Keypoint(coordinates[i], coordinates[i + 1]) for i in range(0, len(coordinates), 3)]
    return keypoints

def save_bodypose(width: int, height: int, coordinates: list):
    if not hasattr(save_bodypose, 'counter'):
        save_bodypose.counter = 0  # Initialize the counter attribute

    canvas = np.zeros((height, width, 3), dtype=np.uint8)
    keypoints = coordinates_to_keypoints(coordinates)
    canvas = draw_bodypose(canvas, keypoints, skel.coco_limbSeq, skel.coco_colors)

    # Save as body_pose_output0000.png, body_pose_output0001.png, ...
    cv2.imwrite('output/body_pose_output%04d.png' % save_bodypose.counter, canvas)
    save_bodypose.counter += 1  # Increment the counter

def main():
    directory = './fixed'
    json_files = [f for f in os.listdir(directory) if f.endswith('.json')]
    if not json_files:
        print("No JSON files found in the directory.")
        return
    
    json_file = os.path.join(directory, random.choice(json_files))
    # json_file = './test_output.json'
    image_path = './output/test'
    print(json_file)

    skeleton_sequences = pjf.array_json_to_Skeleton_Seqences(json_file)
    frame_count = max(len(skeleton_sequences[i].skeletons_frame) for i in range(len(skeleton_sequences)) if skeleton_sequences[i] is not None)
    sliced_list = [skel.get_time_slice_for_Skeleton_Seqences(skeleton_sequences, i) for i in range(frame_count)]

    for i in range(frame_count):
        sliced = sliced_list[i]
        canvas = np.zeros((360, 640, 3), dtype=np.uint8)

        for j, skeleton in enumerate(sliced):
            keypoints = skeleton.keypoints
            skeleton_sequences[j].get_frame(i).keypoints = keypoints
            canvas = draw_bodypose(canvas, keypoints, skel.body_25_limbSeq, skel.body_25_colors)

        cv2.imwrite(image_path + '_' + str(i) + '.png', canvas)

if __name__ == '__main__':
    main()