# Multi-HMR
# Copyright (c) 2024-present NAVER Corp.
# CC BY-NC-SA 4.0 license

import torch
import numpy as np

IMG_NORM_MEAN = [0.485, 0.456, 0.406]
IMG_NORM_STD = [0.229, 0.224, 0.225]


def normalize_rgb(img, imagenet_normalization=True):
    """
    Args:
        - img: np.array - (W,H,3) - np.uint8 - 0/255
    Return:
        - img: np.array - (3,W,H) - np.float - -3/3
    """
    img = img.astype(np.float32) / 255.
    img = np.transpose(img, (2,0,1))
    if imagenet_normalization:
        img = (img - np.asarray(IMG_NORM_MEAN).reshape(3,1,1)) / np.asarray(IMG_NORM_STD).reshape(3,1,1)
    img = img.astype(np.float32)
    return img

def unpatch(data, patch_size=14, c=3, img_size=224):
    # c = 3
    if len(data.shape) == 2:
        c=1
        data = data[:,:,None].repeat([1,1,patch_size**2])

    B,N,HWC = data.shape
    HW = patch_size**2
    c = int(HWC / HW)
    h = w = int(N**.5)
    p = q = int(HW**.5)
    data = data.reshape([B,h,w,p,q,c])
    data = torch.einsum('nhwpqc->nchpwq', data)
    return data.reshape([B,c,img_size,img_size])