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# 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])
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