import pdb

# import config
from pathlib import Path
import sys

PROJECT_ROOT = Path(__file__).absolute().parents[0].absolute()
sys.path.insert(0, str(PROJECT_ROOT))
import os

import cv2
import einops
import numpy as np
import random
import time
import json

# from pytorch_lightning import seed_everything
from preprocess.openpose.annotator.util import resize_image, HWC3
from preprocess.openpose.annotator.openpose import OpenposeDetector

import argparse
from PIL import Image
import torch
import pdb


class OpenPose:
    def __init__(self, gpu_id: int):
        # self.gpu_id = gpu_id
        # torch.cuda.set_device(gpu_id)
        self.preprocessor = OpenposeDetector()

    def __call__(self, input_image, resolution=384):
        # torch.cuda.set_device(self.gpu_id)
        if isinstance(input_image, Image.Image):
            input_image = np.asarray(input_image)
        elif type(input_image) == str:
            input_image = np.asarray(Image.open(input_image))
        else:
            raise ValueError
        with torch.no_grad():
            input_image = HWC3(input_image)
            input_image = resize_image(input_image, resolution)
            H, W, C = input_image.shape
            assert (H == 512 and W == 384), 'Incorrect input image shape'
            pose, detected_map = self.preprocessor(input_image, hand_and_face=False)

            candidate = pose['bodies']['candidate']
            subset = pose['bodies']['subset'][0][:18]
            for i in range(18):
                if subset[i] == -1:
                    candidate.insert(i, [0, 0])
                    for j in range(i, 18):
                        if (subset[j]) != -1:
                            subset[j] += 1
                elif subset[i] != i:
                    candidate.pop(i)
                    for j in range(i, 18):
                        if (subset[j]) != -1:
                            subset[j] -= 1

            candidate = candidate[:18]

            for i in range(18):
                candidate[i][0] *= 384
                candidate[i][1] *= 512

            keypoints = {"pose_keypoints_2d": candidate}

        return keypoints


if __name__ == '__main__':

    model = OpenPose()
    model('./images/model.jpg')