# https://github.com/IDEA-Research/DWPose
# Openpose
# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
# 3rd Edited by ControlNet
# 4th Edited by ControlNet (added face and correct hands)

import copy
import os

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
import cv2
import numpy as np
import torch
from controlnet_aux.util import HWC3, resize_image
from PIL import Image

from . import util
from .wholebody import Wholebody


def draw_pose(pose, H, W):
    bodies = pose["bodies"]
    faces = pose["faces"]
    hands = pose["hands"]
    candidate = bodies["candidate"]
    subset = bodies["subset"]
    canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)

    canvas = util.draw_bodypose(canvas, candidate, subset)

    canvas = util.draw_handpose(canvas, hands)

    canvas = util.draw_facepose(canvas, faces)

    return canvas


class DWposeDetector:
    def __init__(self):
        pass

    def to(self, device):
        self.pose_estimation = Wholebody(device)
        return self

    def cal_height(self, input_image):
        input_image = cv2.cvtColor(
            np.array(input_image, dtype=np.uint8), cv2.COLOR_RGB2BGR
        )

        input_image = HWC3(input_image)
        H, W, C = input_image.shape
        with torch.no_grad():
            candidate, subset = self.pose_estimation(input_image)
            nums, keys, locs = candidate.shape
            # candidate[..., 0] /= float(W)
            # candidate[..., 1] /= float(H)
            body = candidate
        return body[0, ..., 1].min(), body[..., 1].max() - body[..., 1].min()

    def __call__(
        self,
        input_image,
        detect_resolution=512,
        image_resolution=512,
        output_type="pil",
        **kwargs,
    ):
        input_image = cv2.cvtColor(
            np.array(input_image, dtype=np.uint8), cv2.COLOR_RGB2BGR
        )

        input_image = HWC3(input_image)
        input_image = resize_image(input_image, detect_resolution)
        H, W, C = input_image.shape
        with torch.no_grad():
            candidate, subset = self.pose_estimation(input_image)
            nums, keys, locs = candidate.shape
            candidate[..., 0] /= float(W)
            candidate[..., 1] /= float(H)
            score = subset[:, :18]
            max_ind = np.mean(score, axis=-1).argmax(axis=0)
            score = score[[max_ind]]
            body = candidate[:, :18].copy()
            body = body[[max_ind]]
            nums = 1
            body = body.reshape(nums * 18, locs)
            body_score = copy.deepcopy(score)
            for i in range(len(score)):
                for j in range(len(score[i])):
                    if score[i][j] > 0.3:
                        score[i][j] = int(18 * i + j)
                    else:
                        score[i][j] = -1

            un_visible = subset < 0.3
            candidate[un_visible] = -1

            foot = candidate[:, 18:24]

            faces = candidate[[max_ind], 24:92]

            hands = candidate[[max_ind], 92:113]
            hands = np.vstack([hands, candidate[[max_ind], 113:]])

            bodies = dict(candidate=body, subset=score)
            pose = dict(bodies=bodies, hands=hands, faces=faces)

            detected_map = draw_pose(pose, H, W)
            detected_map = HWC3(detected_map)

            img = resize_image(input_image, image_resolution)
            H, W, C = img.shape

            detected_map = cv2.resize(
                detected_map, (W, H), interpolation=cv2.INTER_LINEAR
            )

            if output_type == "pil":
                detected_map = Image.fromarray(detected_map)

            return detected_map, body_score