import numpy as np
import onnxruntime as ort
from .onnxdet import inference_detector
from .onnxpose import inference_pose
from pathlib import Path

class Wholebody:
    def __init__(self, det=None, pose=None, device='cuda:0'):
        device = 'cuda:0'
        if ort.get_device().lower() == 'cpu':
            device = 'cpu'
        providers = ['CPUExecutionProvider'
                 ] if device == 'cpu' else ['CUDAExecutionProvider']
        if det is None or pose is None:
            from huggingface_hub import hf_hub_download
            if det is None or Path(det).exists() is False:
                det = hf_hub_download(repo_id="yzd-v/DWPose",filename='yolox_l.onnx')
            if pose is None or Path(pose).exists() is False:
                pose = hf_hub_download(repo_id="yzd-v/DWPose",filename='dw-ll_ucoco_384.onnx')

        self.session_det = ort.InferenceSession(path_or_bytes=det, providers=providers)
        self.session_pose = ort.InferenceSession(path_or_bytes=pose, providers=providers)
    
    def __call__(self, oriImg):
        det_result = inference_detector(self.session_det, oriImg)
        keypoints, scores = inference_pose(self.session_pose, det_result, oriImg)

        keypoints_info = np.concatenate(
            (keypoints, scores[..., None]), axis=-1)
        # compute neck joint
        neck = np.mean(keypoints_info[:, [5, 6]], axis=1)
        # neck score when visualizing pred
        neck[:, 2:4] = np.logical_and(
            keypoints_info[:, 5, 2:4] > 0.3,
            keypoints_info[:, 6, 2:4] > 0.3).astype(int)
        new_keypoints_info = np.insert(
            keypoints_info, 17, neck, axis=1)
        mmpose_idx = [
            17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3
        ]
        openpose_idx = [
            1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17
        ]
        new_keypoints_info[:, openpose_idx] = \
            new_keypoints_info[:, mmpose_idx]
        keypoints_info = new_keypoints_info

        keypoints, scores = keypoints_info[
            ..., :2], keypoints_info[..., 2]
        
        return keypoints, scores


